Advertisement

Image analysis and machine learning for detecting malaria

Open AccessPublished:January 12, 2018DOI:https://doi.org/10.1016/j.trsl.2017.12.004
      Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.

      Abbreviations:

      GLRLM (Gray Level Run Length Matrix), HoG (Histogram of Gradient), HSV (Hue Saturation Value), IEEE (Institute of Electrical and Electronics Engineers), LBP (Linear Binary Pattern), LED (Light Emitting Diode), NIH (National Institute of Health), NLM (National Library of Medicine), NM (Nearest Mean), P (Plasmodium), PCR (Polymerase Chain Reaction), PLOS (Public Library of Science), QFT (Quaternion Fourier Transform), QPI (Quantitative Phase Imaging), RDT (Rapid Diagnostic Test), RGB (Red Green Blue), RNA (RiboNucleic Acid), SBFSEM (Serial Block-Face Scanning Electron Microscopy), SEM (Scanning Electron Microscope), SightDx (Sight Diagnostics), SROFM (Sub-pixel Resolving Optofluidic Microscope), SUSAN (Smallest Univalue Segment Assimilating Nucleus), SVM (Support Vector Machine), WHO (World Health Organization)

      Introduction

      Malaria is caused by protozoan parasites of the genus Plasmodium that are transmitted through the bites of infected female Anopheles mosquitoes and that infect the red blood cells. Most deaths occur among children in Africa, where a child dies almost every minute from malaria, and where malaria is a leading cause of childhood neuro-disability. According to the World Malaria Report 2016,
      • WHO
      Malaria microscopy quality assurance manual-version 2.
      an estimated 3.2 billion people in 95 countries and territories are at risk of being infected with malaria and developing disease, and 1.2 billion are at high risk (>1 in 1000 chance of getting malaria in a year). There were about 214 million cases of malaria globally in 2016 and about 438,000 malaria deaths. The burden was heaviest in the African region, where an estimated 92%
      • WHO
      World malaria report 2016.
      of all malaria deaths occurred, and in children aged under 5 years, who accounted for more than two thirds of all deaths (see also the malaria death rates from an earlier WHO report in Fig 1). Typical symptoms of malaria include fever, fatigue, headaches, and, in severe cases, seizures and coma, leading to death.
      Fig 1
      Fig 1Worldwide malaria death rates (Source: WHO World Malaria Report 2012).
      Hundreds of millions of blood films are examined every year for malaria, which involves manual counting of parasites and infected red blood cells by a trained microscopist. Accurate parasite counts are essential not only for malaria diagnosis. They are also important for testing for drug-resistance, measuring drug-effectiveness, and classifying disease severity. However, microscopic diagnostics is not standardized and depends heavily on the experience and skill of the microscopist.
      • WHO
      Malaria microscopy quality assurance manual-version 2.
      It is common for microscopists in low-resource settings to work in isolation, with no rigorous system in place that can ensure the maintenance of their skills and thus diagnostic quality.
      • WHO
      Malaria microscopy quality assurance manual-version 2.
      This leads to incorrect diagnostic decisions in the field.
      • WHO
      Malaria microscopy quality assurance manual-version 2.
      For false-negative cases, this leads to unnecessary use of antibiotics, a second consultation, lost days of work, and in some cases progression into severe malaria. For false-positive cases, a misdiagnosis entails unnecessary use of anti-malaria drugs and suffering from their potential side effects, such as nausea, abdominal pain, diarrhea, and sometimes severe complications.
      This sober analysis of malaria diagnosis has prompted efforts to perform malaria diagnosis automatically. Automatic parasite counting has several advantages compared with manual counting: (1) it provides a more reliable and standardized interpretation of blood films, (2) it allows more patients to be served by reducing the workload of the malaria field workers, and (3) it can reduce diagnostic costs. Several key processing steps are typically required to quantify parasitemia automatically. First, digital blood slide images need to be acquired, which often requires preprocessing to normalize for lighting or staining variations. In a second step, blood cells or parasites need to be detected. For blood cells, this typically implies cell segmentation to identify individual cells in cell clumps to obtain accurate cell counts. In a third step, after cell detection and segmentation, features are computed to describe the typical visual appearance of infected and uninfected blood cells. In a final classification step, a classifier, who has been trained on an independent and typically manually annotated training set, then discriminates between infected and uninfected cells. Once the number of infected and uninfected cells is known, computation of parasitemia is a straightforward mathematical equation, which includes clinical parameters such as hematocrit value, for example.
      The prospects of automating malaria diagnosis with its obvious advantages has attracted many researchers, especially in the last decade. The publications reflect all the major developments we have seen in the areas of automatic pattern recognition and machine learning in the last years. Our article will give an overview of the articles that have been published, using the processing steps mentioned above as a framework and guide. This is not the first survey article on the subject. In fact, several survey articles have already been published before, which bear testimony to both the importance of automated malaria diagnosis and the research dynamics and rapid system development. We refer readers in particular to the following surveys for additional information about the background of automatic malaria diagnosis and the image processing and machine learning methods used for automated microscopy diagnosis of malaria.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Computer vision for microscopy diagnosis of malaria.
      • Das D.
      • Mukherjee R.
      • Chakraborty C.
      Computational microscopic imaging for malaria parasite detection: a systematic review.
      • Jan Z.
      • Khan A.
      • Sajjad M.
      • Muhammad K.
      • Rho S.
      • Mehmood I.
      A review on automated diagnosis of malaria parasite in microscopic blood smears images.
      In addition, more specific surveys have been published on cell features for malaria parasite detection,
      • Devi S.S.
      • Sheikh S.A.
      • Laskar R.H.
      Erythrocyte features for malaria parasite detection in microscopic images of thin blood smear: a review.
      on malaria diagnosis,
      • Tangpukdee N.
      • Duangdee C.
      • Wilairatana P.
      • Krudsood S.
      Malaria diagnosis: a brief review.
      on malaria diagnostic tools,
      • Wongsrichanalai C.
      • Barcus M.J.
      • Muth S.
      • Sutamihardja A.
      • Wernsdorfer W.H.
      A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT).
      and on alternatives to conventional microscopy.
      • Hänscheid T.
      Diagnosis of malaria: a review of alternatives to conventional microscopy.
      The purpose of our article is not to replace these surveys, but rather to complement them and to provide the latest update of the state of the art in image analysis and machine learning for malaria diagnosis as it presents itself at the end of the year 2017. With about 170 literature citations, we have collected more references compared with the other surveys. We had the goal to include also maybe lesser known publications to provide a historical documentation of the work done. In addition, we included a section on deep learning, which is the latest development in malaria diagnosis and which arguably has the potential to render many of the old approaches obsolete, similar to the development in other imaging application areas. There have also been many developments in hardware for automatic malaria diagnosis, which are however out of the scope of this article and deserve a separate article
      • Lee S.A.
      • Leitao R.
      • Zheng G.
      • Yang S.
      • Rodriguez A.
      • Yang C.
      Color capable sub-pixel resolving optofluidic microscope and its application to blood cell imaging for malaria diagnosis.
      • Vink J.
      • Laubscher M.
      • Vlutters R.
      • et al.
      An automatic vision-based malaria diagnosis system.
      • Srivastava B.
      • Anvikar A.R.
      • Ghosh S.K.
      • et al.
      Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria.
      • Prescott W.R.
      • Jordan R.G.
      • Grobusch M.P.
      • et al.
      Performance of a malaria microscopy image analysis slide reading device.
      • Herrera S.
      • Vallejo A.F.
      • Quintero J.P.
      • Arévalo-Herrera M.
      • Cancino M.
      • Ferro S.
      Field evaluation of an automated RDT reader and data management device for Plasmodium falciparum/Plasmodium vivax malaria in endemic areas of Colombia.
      . Nevertheless, we devote a section to rapid diagnostic tests (RDTs) for malaria diagnosis because they are also widely used in the field. The bulk of our articles have been collected from the Journal of Microscopy, Malaria Journal, and PLOS ONE, including a few articles from Nature and others. We have also collected publications from Institute of Electrical and Electronics Engineers (IEEE) conferences and other proceedings published by Springer and Elsevier. Furthermore, we have organized the articles into sections for preprocessing, cell detection and segmentation, feature computation, and classification. We have also added a separate section about deep learning and an extensive section about mobile smartphone applications for malaria diagnosis. A discussion of the latest developments and our conclusion mark the end of this article.

      Malaria

      There are 5 Plasmodium species that cause malaria in human: Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, Plasmodium ovale, and Plasmodium knowlesi. The 2 most common species are P. falciparum and P. vivax. P. falciparum is the most severe form and is responsible for most malaria-related deaths globally.
      • WHO
      Malaria microscopy quality assurance manual-version 2.
      P. falciparum is the most prevalent malaria parasite in sub-Saharan Africa, accounting for 99% of estimated malaria cases in 2016. Outside of Africa, P. vivax is the predominant parasite in the WHO Region of the Americas, representing 64% of malaria cases, and is above 30% in the WHO Southeast Asia and 40% in the Eastern Mediterranean regions.
      • WHO
      Malaria microscopy quality assurance manual-version 2.
      Each of these parasite species goes through stages during their development cycle (48 hours), which gives the parasites a different visual appearance that can be observed under the microscope. In chronologic order, these stages are the ring stage, trophozoite stage, schizont stage, and gametocyte stage. Fig 2 shows typical examples of all stages for each species.
      Fig 2
      Fig 2Five different human malaria Plasmodium species and their life stages in thin blood film (Source: K. Silamut and CDC).
      In nonsevere malaria, mostly the young stages (<24 hours old) of P. falciparum are present in the peripheral blood, whereas for severe malaria all stages can be present in the peripheral blood. For P. falciparum, the trophozoite-infected red blood cells disappear from the peripheral blood circulation by attachment to the walls of capillaries inside vital organs, which is a process called sequestration. If the capillaries are blocked for newly infected cells by already attached cells, more mature parasite stages (trophozoites and schizonts) will be visible in the peripheral blood, which indicates a severe infection and a bad prognosis.
      For P. falciparum, ring stages have a visible cytoplasm and 1 or 2 small chromatin dots. The infected blood cells are not enlarged but can feature multiple infections. P. falciparum trophozoites are rarely seen in peripheral blood smears. The cytoplasm of mature trophozoites tends to be more dense than younger rings, trophozoites can appear round in shape with brown malarial pigment inside, (Centers for Disease Control and Prevention (CDC)). P. falciparum schizonts are also seldomly seen in peripheral blood. They are displaying more than 2 and up to 32 nuclei (merozoites) with dark brown pigment clumped in the middle. Gametocytes of P. falciparum have a crescent or sausage shape, and can be seen in the blood smear 1 week after a parasite infection. The chromatin is visible as a single mass or is diffuse. For more information about P. falciparum morphology, see for example References
      • Silamut K.
      • White N.
      Relation of the stage of parasite development in the peripheral blood to prognosis in severe falciparum malaria.
      • Silamut K.
      • Phu N.H.
      • Whitty C.
      • et al.
      A quantitative analysis of the microvascular sequestration of malaria parasites in the human brain.
      . Similar observations can be made for the stages of the other parasite species. For example, for P. vivax, host cells are often enlarged and have irregular shape. Trophozoites are amoeboid in shape with malaria pigment seen, and for severe infections multiple infections of single blood cells are not uncommon. For P. malariae, host cells are not enlarged. Trophozoites have a strong tendency to form a band with malarial pigment scattered along across the diameter of infected red blood cells. Multiple infections are extremely rare for P. malariae. On the other hand, for P. ovale, host cells are slightly enlarged and have an oval shape with tufted ends, often fimbriated. Parasites are slightly enlarged and trophozoites are amoeboid in shape with malarial pigment. Multiple infections of a single cell are more common than for P. vivax. For P. knowlesi, infected red blood cells do not appear enlarged. The parasite erythocytic cycle is only 24 hours, which is shorter than P. falciparum's cycle (48 hours) and much shorter than P. malariae's cycle (72 hours), which will lead to the same stage seen in peripheral blood every day at a given time. The morphology of P. knowlesi parasites is similar to P. malariae. Trophozoites can feature malarial pigment spread inside, band form may be seen like P. malariae, but their cytoplasm is more irregular, and multiple parasites infecting 1 single red blood cell can be seen like in P. falciparum.
      Fig 3 shows 2 examples of different parasite stages in the same thin blood slide image. In the first slide image, P. falciparum trophozoites and gametocytes can be seen together with white blood cells. The latter are larger and have a pronounced nucleus compared with the many red blood cells in the image. In the second image, P. falciparum ring stages are together with schizonts. In addition, other objects such as parasite outside cells and staining noise are visible in both images. Staining noise in particular can be confused with parasites by an unexperienced microscopist.
      Fig 3
      Fig 3Parasite stages in a single thin blood smear.

      Malaria Diagnosis

      Malaria is a curable disease, with drugs available for treatment, including drugs that can help prevent malaria infections in travelers to malaria-prone regions. However, there exists no effective vaccine against malaria yet, although this is an area of active research and field studies. Once infected, malaria is a rapidly progressing disease, with a serious risk of developing into severe and cerebral malaria with neurologic symptoms for P. falciparum infections. Therefore, a timely diagnosis of malaria is very important. Although malaria can be diagnosed in many different ways, there is room for improvement for current malaria diagnostic tests including reducing cost, increasing specificity, and improving ease of use. Because automated malaria diagnosis for resource-poor settings is the main topic of this survey, we have devoted 2 subsections to light microscopy and RDTs, which are by far the 2 most heavily used diagnostic means in these areas. We also briefly discuss the other options for malaria diagnosis, although they are arguably less suited for the conditions in remote malaria regions. For more information about malaria diagnosis, we refer readers to the surveys in
      • Tangpukdee N.
      • Duangdee C.
      • Wilairatana P.
      • Krudsood S.
      Malaria diagnosis: a brief review.
      ,
      • Hänscheid T.
      Diagnosis of malaria: a review of alternatives to conventional microscopy.
      and the following references:
      • Wongsrichanalai C.
      • Barcus M.J.
      • Muth S.
      • Sutamihardja A.
      • Wernsdorfer W.H.
      A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT).
      • Parsel S.M.
      • Gustafson S.A.
      • Friedlander E.
      • et al.
      Malaria over-diagnosis in Cameroon: diagnostic accuracy of Fluorescence and Staining Technologies (FAST) Malaria Stain and LED microscopy versus Giemsa and bright field microscopy validated by polymerase chain reaction.
      • Lee S.A.
      • Leitao R.
      • Zheng G.
      • Yang S.
      • Rodriguez A.
      • Yang C.
      Color capable sub-pixel resolving optofluidic microscope and its application to blood cell imaging for malaria diagnosis.
      .
      Detecting the presence of parasites is the key to malaria diagnosis. In addition, identifying the parasite species and presence of potentially mixed infections is important, as well as the observation of the stage development of P. falciparum parasites in relation to the severity of the disease. Counting parasites for determining the level of parasitemia is not only important for identifying an infection and measuring its severity, it also allows monitoring patients by measuring drug efficacy and potential drug resistance.

       Light microscopy

      The current gold-standard method for malaria diagnosis in the field is light microscopy of blood films, which is the main focus of this article. Although other forms of diagnosis exist and have become popular in recent years, in particular RDTs, microscopy remains the most popular diagnostic tool, especially in resource-poor settings. With microscopy, all parasite species can be detected. It allows computing the level of parasitemia, clearing a patient after a successful treatment, and monitoring drug resistance. Furthermore, it is less expensive than other methods and widely available. However, its biggest disadvantages are the extensive training required for a microscopist to become a proficient malaria slide reader, the high cost of training and employing, maintaining skills, and the large component of manual work involved.
      To diagnose malaria under a microscope, a drop of the patient's blood is applied to a glass slide, which is then immersed in a staining solution to make parasites more easily visible under a conventional light microscope, usually with a 100× oil objective. Two different types of blood smears are typically prepared for malaria diagnosis: thick and thin smears.
      • Dowling M.
      • Shute G.
      A comparative study of thick and thin blood films in the diagnosis of scanty malaria parasitaemia.
      A thick smear is used to detect the presence of parasites in a drop of blood. Thick smears allow a more efficient detection of parasites than thin smears, with an 11 times higher sensitivity.
      • Jan Z.
      • Khan A.
      • Sajjad M.
      • Muhammad K.
      • Rho S.
      • Mehmood I.
      A review on automated diagnosis of malaria parasite in microscopic blood smears images.
      On the other hand, thin smears, which are the result of spreading the drop of blood across the glass slide, have other advantages. They allow the examiner to identify malaria species and recognize parasite stages more easily.
      The actual microscopic examination of a single blood slide, including quantitative parasite detection and species identification, takes a trained microscopist 15–30 minutes. Considering that hundreds of thousands of blood slides are manually inspected for malaria every year, this amounts to a huge economic effort required for malaria diagnosis.

       Rapid diagnostic tests

      The main advantage of microscopic malaria diagnosis lies in its low direct cost, which gives it a distinct advantage in resource-poor settings.
      • WHO
      Malaria microscopy quality assurance manual-version 2.
      Other existing diagnostic methods, and any new method, have to prove that they can provide the same ease of use and price point as microscopy given the limited financial resources typically available in malaria-prone regions. Arguably the only and main competitor in this sense are RDTs. They detect evidence of malaria parasites (antigens) and take about 10–15 minutes to process. Their detection sensitivity is lower but comparable with manual microscopy, and they do not require any special equipment and require only minimal training.
      Although RDTs are currently more expensive than microscopy in high-burden areas,
      • WHO
      Determining cost effectiveness of malaria rapid diagnostic tests in rural areas with high prevalence.
      a valid question is whether these tests can replace microscopy in the near future. At the time of this writing, according to WHO,
      • WHO
      Malaria microscopy quality assurance manual-version 2.
      more countries use microscopy more than they use RDTs.
      • WHO
      World malaria report 2016.
      RDTs are used more in rural areas where microscopy is not available. About 47% of malaria tests in malaria endemic countries worldwide were made by RDT.
      • WHO
      World malaria report 2016.
      The use of RDTs, however, does not eliminate the need for malaria microscopy. A major disadvantage is that RDTs do not provide quantification of the results. Therefore, at this point in time, microscopy and RDTs are more complementing each other than one replacing the other.

       Other tests

      Several methods for diagnosing malaria are available. Important criteria are cost per test, sensitivity and specificity of the method, time per test, and the required skill level of the user. Furthermore, quantification of the number of infected red blood cells is important as a prognostic indicator.
      • Vink J.
      • Laubscher M.
      • Vlutters R.
      • et al.
      An automatic vision-based malaria diagnosis system.
      • Polymerase chain reaction (PCR). A molecular method called PCR has shown higher sensitivity and specificity than conventional microscopic examination of stained peripheral blood smears.
        • Tangpukdee N.
        • Duangdee C.
        • Wilairatana P.
        • Krudsood S.
        Malaria diagnosis: a brief review.
        In fact, it is considered the most accurate among all tests. It can detect very low parasite concentrations in the blood and can differentiate species. However, PCR is a complex high-cost technology that takes many hours to process by trained staff. According to Tangpukdee et al.,
        • Tangpukdee N.
        • Duangdee C.
        • Wilairatana P.
        • Krudsood S.
        Malaria diagnosis: a brief review.
        PCR is not routinely implemented in developing countries because of the complexity of the testing and the lack of resources to perform these tests adequately and routinely. Quality control and equipment maintenance are also essential for the PCR technique, so that it may not be suitable for malaria diagnosis in remote rural areas or even in routine clinical diagnostic settings.
      • Fluorescent microscopy. Quantitative buffy coat is a laboratory test to detect infection with malaria or other blood parasites, using fluorescent microscopy. A fluorescent dye makes parasites visible under ultraviolet light. According to Adeoye and Nga,
        • Adeoye G.
        • Nga I.
        Comparison of Quantitative Buffy Coat technique (QBC) with Giemsa-stained Thick Film (GTF) for diagnosis of malaria.
        this test is more sensitive than the conventional thick smear. Nowadays, portable fluorescent microscopes with fluorescent reagent to label parasites, are available commercially. Although the quantitative buffy coat technique is simple, reliable, and user friendly, it requires specialized instrumentation, is more costly than conventional light microscopy, and is poor at determining species and numbers of parasites.
        • Tangpukdee N.
        • Duangdee C.
        • Wilairatana P.
        • Krudsood S.
        Malaria diagnosis: a brief review.
      • Flow cytometry. This is a laser-based cell counting and detection method that allows to profile thousands of cells per second. Although flow cytometry offers automated parasitemia counts, this is offset by a rather low sensitivity. Flow cytometry is less suitable as a diagnostic technique in the field, when a direct answer is required for treatment decisions. However, in developed countries, it can be applied in the clinical setting for accurate counting of parasite numbers, for instance in the follow-up of drug treatment.
        • Janse C.J.
        • Van Vianen P.H.
        Flow cytometry in malaria detection.

      Staining Methods

      More than 100 years ago, Giemsas stain (1902) was applied for the first time for the diagnosis of malaria. Since then, it received increased attention. Because of its low cost, its high sensitivity, and specificity, it is currently widely used in microscopical malaria examinations.
      • Keiser J.
      • Utzinger J.
      • Premji Z.
      • Yamagata Y.
      • Singer B.H.
      Acridine orange for malaria diagnosis: its diagnostic performance, its promotion and implementation in Tanzania, and the implications for malaria control.
      However, Giemsa staining requires multiple reagents, experienced personal, and is labor-intensive and time-consuming (it typically requires at least 45 minutes to stain a slide
      • Keiser J.
      • Utzinger J.
      • Premji Z.
      • Yamagata Y.
      • Singer B.H.
      Acridine orange for malaria diagnosis: its diagnostic performance, its promotion and implementation in Tanzania, and the implications for malaria control.
      ).
      Other stains have been used, too, like Field stain that significantly reduces the staining time, although it requires drying of samples before and during staining.
      • Houwen B.
      Blood film preparation and staining procedures.
      However there are also disadvantages with Field's stain, especially in under-resourced health centers in which the stain might be used. Poor blood preparations often result in the generation of artifacts commonly mistaken for malaria parasites, such as bacteria, fungi, stain precipitation, dirt, and cell debris. These can frequently cause false-positive readings.
      Another stain is Leishman's stain (1901), which has a high sensitivity, is cheap, and relatively easy to perform. Among the other stains being used is, for example, the Wright-Giemsa stain, which is a combination of Wright and Giemsa stain, and where the former facilitates the differentiation of blood cell types.
      In 1970s, Sodeman et al.
      • Shute G.
      • Sodeman T.
      Identification of malaria parasites by fluorescence microscopy and acridine orange staining.
      investigated the effect of fluorochrome staining in identifying the malaria parasites at low-level infection. It has been shown that fluorochrome staining is more sensitive and less time-consuming than Romanowsky and Giemsa staining methods
      • Suwalka I.
      • Sanadhya A.
      • Mathur A.
      • Chouhan M.S.
      Identify malaria parasite using pattern recognition technique.
      • Kawamoto F.
      Rapid diagnosis of malaria by fluorescence microscopy with light microscope and interference filter.
      • Wongsrichanalai C.
      • Kawamotob F.
      Fluorescent microscopy and fluorescent labelling for malaria diagnosis.
      but requires considerable practice and training, and suffers from artifacts including photobleaching and phototoxicity.
      • Diaspro A.
      • Chirico G.
      • Usai C.
      • Ramoino P.
      • Dobrucki J.
      Photobleaching.
      • Waters J.C.
      Accuracy and precision in quantitative fluorescence microscopy.
      Moreover, fluorescence microscopes are more expensive than standard light microscopes, which is a factor in tropical resource-poor regions where malaria is endemic.
      • Shute G.
      • Sodeman T.
      Identification of malaria parasites by fluorescence microscopy and acridine orange staining.
      • Kawamoto F.
      Rapid diagnosis of malaria by fluorescence microscopy with light microscope and interference filter.
      • Guy R.
      • Liu P.
      • Pennefather P.
      • Crandall I.
      The use of fluorescence enhancement to improve the microscopic diagnosis of falciparum malaria.
      Table I shows the blood smear types and staining techniques used for the approaches published in the literature. Clearly, the vast majority of publications has been for thin smears. Certainly, 1 reason for this lies in the fact that thin smears allow to determine the parasite species and stages more easily, in addition to the parasitemia. So, in some sense, thin smears are more versatile and contain more information. Another important reason is probably that the presence of red blood cells gives the problem of parasite detection more structure, and makes the problem easier to a certain degree, as parasites need to be detected only inside cells. For thick films, parasite detection may be harder because of noise and staining artifacts that can lead to false positives. Nevertheless, because of the importance of thick smears for practical malaria diagnosis, it is very likely that more approaches for thick films will be implemented in the future. However, if convincing optical hardware solutions are found to scan multiple fields in thin smears and achieve a sensitivity comparable with thick smears, then this may be a moot point.
      • Zou L.
      • Chen J.
      • Zhang J.
      • Garcia N.
      Malaria cell counting diagnosis within large field of view.
      • Kaewkamnerd S.
      • Uthaipibull C.
      • Intarapanich A.
      • Pannarut M.
      • Chaotheing S.
      • Tongsima S.
      An automatic device for detection and classification of malaria parasite species in thick blood film.
      Table IBlood smear types and staining methods for malaria diagnosis
      Blood smearStaining
      ThinGiemsa
      • Wongsrichanalai C.
      • Barcus M.J.
      • Muth S.
      • Sutamihardja A.
      • Wernsdorfer W.H.
      A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT).
      • Janse C.J.
      • Van Vianen P.H.
      Flow cytometry in malaria detection.
      • Yang D.
      • Subramanian G.
      • Duan J.
      • et al.
      A portable image-based cytometer for rapid malaria detection and quantification.
      • Mavandadi S.
      • Dimitrov S.
      • Feng S.
      • et al.
      Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.
      • Linder N.
      • Turkki R.
      • Walliander M.
      • et al.
      A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears.
      • Mohammed H.A.
      • Abdelrahman I.A.M.
      Detection and classification of malaria in thin blood slide images.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      A colour normalization method for giemsa-stained blood cell images.
      • Di Rubeto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Segmentation of blood images using morphological operators.
      • Halim S.
      • Bretschneider T.R.
      • Li Y.
      • Preiser P.R.
      • Kuss C.
      Estimating malaria parasitaemia from blood smear images.
      • Anggraini D.
      • Nugroho A.S.
      • Pratama C.
      • Rozi I.E.
      • Pragesjvara V.
      • Gunawan M.
      Automated status identification of microscopic images obtained from malaria thin blood smears using Bayes decision: a study case in Plasmodium falciparum.
      • Kareem S.
      • Morling R.C.
      • Kale I.
      A novel method to count the red blood cells in thin blood films.
      • Kareem S.
      • Kale I.
      • Morling R.C.
      Automated malaria parasite detection in thin blood films: a hybrid illumination and color constancy insensitive, morphological approach.
      • Nasir A.A.
      • Mashor M.
      • Mohamed Z.
      Segmentation based approach for detection of malaria parasites using moving k-means clustering.
      • Malihi L.
      • Ansari-Asl K.
      • Behbahani A.
      Malaria parasite detection in giemsa-stained blood cell images.
      • Berge H.
      • Taylor D.
      • Krishnan S.
      • Douglas T.S.
      Improved red blood cell counting in thin blood smears.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Automatic thresholding of infected blood images using granulometry and regional extrema.
      • Kareem S.
      • Kale I.
      • Morling R.C.
      Automated P. falciparum detection system for post-treatment malaria diagnosis using modified annular ring ratio method.
      • Zou L.
      • Chen J.
      • Zhang J.
      • Garcia N.
      Malaria cell counting diagnosis within large field of view.
      • Mushabe M.C.
      • Dendere R.
      • Douglas T.S.
      Automated detection of malaria in Giemsa-stained thin blood smears.
      • Savkare S.
      • Narote S.
      Automated system for malaria parasite identification.
      • Mehrjou A.
      • Abbasian T.
      • Izadi M.
      Automatic malaria diagnosis system.
      • Gatc J.
      • Maspiyanti F.
      • Sarwinda D.
      • Arymurthy A.M.
      Plasmodium parasite detection on red blood cell image for the diagnosis of malaria using double thresholding.
      • Nanoti A.
      • Jain S.
      • Gupta C.
      • Vyas G.
      Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear.
      • Maiseli B.
      • Mei J.
      • Gao H.
      • Yin S.
      An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices.
      • Liang Z.
      • Powell A.
      • Ersoy I.
      • et al.
      CNN-based image analysis for malaria diagnosis.
      • Bibin D.
      • Nair M.S.
      • Punitha P.
      Malaria parasite detection from peripheral blood smear images using deep belief networks.
      • Adi K.
      • Pujiyanto S.
      • Gernowo R.
      • Pamungkas A.
      • Putranto A.B.
      Identifying the developmental phase of Plasmodium falciparum in malaria-infected red blood cells using adaptive color segmentation and back propagation neural network.
      • Dallet C.
      • Kareem S.
      • Kale I.
      Real time blood image processing application for malaria diagnosis using mobile phones.
      • Elter M.
      • Haßlmeyer E.
      • Zerfaß T.
      Detection of malaria parasites in thick blood films.
      • Fang Y.
      • Xiong W.
      • Lin W.
      • Chen Z.
      Unsupervised malaria parasite detection based on phase spectrum.
      • May Z.
      • Aziz S.S.A.M.
      • Salamat R.
      Automated quantification and classification of malaria parasites in thin blood smears.
      • Sheikhhosseini M.
      • Rabbani H.
      • Zekri M.
      • Talebi A.
      Automatic diagnosis of malaria based on complete circle–ellipse fitting search algorithm.
      • Díaz G.
      • González F.A.
      • Romero E.
      A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Parasite detection and identification for automated thin blood film malaria diagnosis.
      • Brückner M.
      • Becker K.
      • Popp J.
      • Frosch T.
      Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells.
      • Sakaguchi M.
      • Miyazaki N.
      • Fujioka H.
      • Kaneko O.
      • Murata K.
      Three-dimensional analysis of morphological changes in the malaria parasite infected red blood cell by serial block-face scanning electron microscopy.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Analysis of infected blood cell images using morphological operators.
      • Sio S.W.
      • Sun W.
      • Kumar S.
      • et al.
      MalariaCount: an image analysis-based program for the accurate determination of parasitemia.
      • Savkare S.
      • Narote S.
      Automatic system for classification of erythrocytes infected with malaria and identification of parasite's life stage.
      • Srivastava B.
      • Anvikar A.R.
      • Ghosh S.K.
      • et al.
      Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria.
      • Prescott W.R.
      • Jordan R.G.
      • Grobusch M.P.
      • et al.
      Performance of a malaria microscopy image analysis slide reading device.
      • Purwar Y.
      • Shah S.L.
      • Clarke G.
      • Almugairi A.
      • Muehlenbachs A.
      Automated and unsupervised detection of malarial parasites in microscopic images.
      • Ma C.
      • Harrison P.
      • Wang L.
      • Coppel R.L.
      Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears.
      • Proudfoot O.
      • Drew N.
      • Scholzen A.
      • Xiang S.
      • Plebanski M.
      Investigation of a novel approach to scoring Giemsa-stained malaria-infected thin blood films.
      • Daniel T.
      • Pierre E.
      • Emmanuel T.
      • Philippe B.
      Automated diagnosis of malaria in tropical areas using 40X microscopic images of blood smears.
      • Kim J.D.
      • Nam K.M.
      • Park C.Y.
      • Kim Y.S.
      • Song H.J.
      Automatic detection of malaria parasite in blood images using two parameters.
      • Abbas N.
      • Saba T.
      • Mohamad D.
      • Rehman A.
      • Almazyad A.S.
      • Al-Ghamdi J.S.
      Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Morphological image processing for evaluating malaria disease.
      • Prasad K.
      • Winter J.
      • Bhat U.M.
      • Acharya R.V.
      • Prabhu G.K.
      Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images.
      • Ghosh P.
      • Bhattacharjee D.
      • Nasipuri M.
      • Basu D.K.
      Medical aid for automatic detection of malaria.
      • Daz G.
      • Gonzalez F.
      • Romero E.
      Infected cell identification in thin blood images based on color pixel classification: comparison and analysis.
      • Raviraja S.
      • Bajpai G.
      • Sharma S.K.
      Analysis of detecting the malarial parasite infected blood images using statistical based approach.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Malaria parasite detection in peripheral blood images.
      • Le M.T.
      • Bretschneider T.R.
      • Kuss C.
      • Preiser P.R.
      A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears.
      • Savkare S.
      • Narote S.
      Automatic detection of malaria parasites for estimating parasitemia.
      • Suradkar P.T.
      Detection of malarial parasite in blood using image processing.
      • Aimi Salihah A.N.
      • Yusoff M.
      • Zeehaida M.
      Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering.
      • Khan M.I.
      • Acharya B.
      • Singh B.K.
      • Soni J.
      Content based image retrieval approaches for detection of malarial parasite in blood images.
      • Khatri K.
      • Ratnaparkhe V.
      • Agrawal S.
      • Bhalchandra A.
      Image processing approach for malaria parasite identification.
      • Hirimutugoda Y.
      • Wijayarathna G.
      Image analysis system for detection of red cell disorders using artificial neural networks.
      • Razzak M.I.
      Malarial parasite classification using recurrent neural network.
      • Ajala F.
      • Fenwa O.
      • Aku M.
      Comparative analysis of different types of malaria diseases using first order features.
      • Pamungkas A.
      • Adi K.
      • Gernowo R.
      Identification of Plasmodium falciparum development phase in malaria infected red blood cells using adaptive color segmentation and decision tree based classification.
      • Razzak M.I.
      Automatic detection and classification of malarial parasite.
      • Ahirwar N.
      • Pattnaik S.
      • Acharya B.
      Advanced image analysis based system for automatic detection and classification of malarial parasite in blood images.
      • Anand P.R.
      • Bajpai G.
      • Bhaskar V.
      • Job S.M.
      Detection of the malarial parasite infected blood images by 3D-analysis of the cell curved surface.
      • Cesario M.
      • Lundon M.
      • Luz S.
      • Masoodian M.
      • Rogers B.
      Mobile support for diagnosis of communicable diseases in remote locations.
      • Chavan S.N.
      • Sutkar A.M.
      Malaria disease identification and analysis using image processing.
      • Chayadevi M.
      • Raju G.
      Usage of art for automatic malaria parasite identification based on fractal features.
      • Ghate D.A.
      • Jadhav C.
      Automatic detection of malaria parasite from blood images.
      • Gitonga L.
      • Memeu D.M.
      • Kaduki K.A.
      • Kale M.A.C.
      • Muriuki N.S.
      Determination of plasmodium parasite life stages and species in images of thin blood smears using artificial neural network.
      • Khan N.A.
      • Pervaz H.
      • Latif A.K.
      • et al.
      Unsupervised identification of malaria parasites using computer vision.
      • Premaratne S.P.
      • Karunaweera N.D.
      • Fernando S.
      • Perera W.S.R.
      • Rajapaksha R.
      A neural network architecture for automated recognition of intracellular malaria parasites in stained blood films.
      • Soni J.
      Advanced image analysis based system for automatic detection of malarial parasite in blood images using SUSAN approach.
      • Soni J.
      • Mishra N.
      • Kamargaonkar N.
      Automatic difference between RBC and malaria parasites based on morphology with first order features using image processing.
      • Suryawanshi M.S.
      • Dixit V.
      Improved technique for detection of malaria parasites within the blood cell images.
      • Walliander M.
      • Turkki R.
      • Linder N.
      • et al.
      Automated segmentation of blood cells in Giemsa stained digitized thin blood films.
      • Von Mühlen A.
      Computer image analysis of malarial Plasmodium vivax in human red blood cells.
      • Špringl V.
      Automatic malaria diagnosis through microscopy imaging.
      • Memeu D.M.
      A rapid malaria diagnostic method based on automatic detection and classification of plasmodium parasites in stained thin blood smear images.
      Leishman
      • Khan N.A.
      • Pervaz H.
      • Latif A.K.
      • et al.
      Unsupervised identification of malaria parasites using computer vision.
      • Makkapati V.V.
      • Rao R.M.
      Segmentation of malaria parasites in peripheral blood smear images.
      • Das D.
      • Ghosh M.
      • Chakraborty C.
      • Maiti A.K.
      • Pal M.
      Probabilistic prediction of malaria using morphological and textural information.
      • Ghosh M.
      • Das D.
      • Chakraborty C.
      • Ray A.K.
      Plasmodium vivax segmentation using modified fuzzy divergence.
      • Makkapati V.V.
      • Rao R.M.
      Ontology-based malaria parasite stage and species identification from peripheral blood smear images.
      • Das D.
      • Maiti A.
      • Chakraborty C.
      Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.
      • Das D.K.
      • Ghosh M.
      • Pal M.
      • Maiti A.K.
      • Chakraborty C.
      Machine learning approach for automated screening of malaria parasite using light microscopic images.
      • Bibin D.
      • Punitha P.
      Stained blood cell detection and clumped cell segmentation useful for malaria parasite diagnosis.
      • Devi S.S.
      • Sheikh S.A.
      • Talukdar A.
      • Laskar R.H.
      Malaria infected erythrocyte classification based on the histogram features using microscopic images of thin blood smear.
      • Damahe L.B.
      • Krishna R.
      • Janwe N.
      Segmentation based approach to detect parasites and RBCs in blood cell images.
      • Das D.K.
      • Maiti A.K.
      • Chakraborty C.
      Textural pattern classification of microscopic images for malaria screening.
      • Ghosh M.
      • Das D.
      • Chakraborty C.
      • Ray A.K.
      Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach.
      • Kumar A.
      • Choudhary A.
      • Tembhare P.
      • Pote C.
      Enhanced identification of malarial infected objects using Otsu algorithm from thin smear digital images.
      • Maity M.
      • Maity A.K.
      • Dutta P.K.
      • Chakraborty C.
      A web-accessible framework for automated storage with compression and textural classification of malaria parasite images.
      • Panchbhai V.V.
      • Damahe L.B.
      • Nagpure A.V.
      • Chopkar P.N.
      RBCs and parasites segmentation from thin smear blood cell images.
      Leishman-Methylene blue
      • Mulay H.D.
      • Murthy T.D.
      • Nerune S.M.
      • Amrutha M.
      New methylene blue stain for malaria detection on thin smears.
      Combination of DNA and RNA fluorescent
      • Eshel Y.
      • Houri-Yafin A.
      • Benkuzari H.
      • et al.
      Evaluation of the Parasight platform for malaria diagnosis.
      Wright
      • Skandarajah A.
      • Reber C.D.
      • Switz N.A.
      • Fletcher D.A.
      Quantitative imaging with a mobile phone microscope.
      • Dong Y.
      • Jiang Z.
      • Shen H.
      • et al.
      Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells.
      • Muralidharan V.
      • Dong Y.
      • Pan W.D.
      A comparison of feature selection methods for machine learning based automatic malarial cell recognition in wholeslide images.
      Fluorochrome
      • Parsel S.M.
      • Gustafson S.A.
      • Friedlander E.
      • et al.
      Malaria over-diagnosis in Cameroon: diagnostic accuracy of Fluorescence and Staining Technologies (FAST) Malaria Stain and LED microscopy versus Giemsa and bright field microscopy validated by polymerase chain reaction.
      • Shute G.
      • Sodeman T.
      Identification of malaria parasites by fluorescence microscopy and acridine orange staining.
      • Kawamoto F.
      Rapid diagnosis of malaria by fluorescence microscopy with light microscope and interference filter.
      • Wongsrichanalai C.
      • Kawamotob F.
      Fluorescent microscopy and fluorescent labelling for malaria diagnosis.
      • Guy R.
      • Liu P.
      • Pennefather P.
      • Crandall I.
      The use of fluorescence enhancement to improve the microscopic diagnosis of falciparum malaria.
      • Breslauer D.N.
      • Maamari R.N.
      • Switz N.A.
      • Lam W.A.
      • Fletcher D.A.
      Mobile phone based clinical microscopy for global health applications.
      Romanowsky
      • Suwalka I.
      • Sanadhya A.
      • Mathur A.
      • Chouhan M.S.
      Identify malaria parasite using pattern recognition technique.
      Acridine orange (AO)
      • Vink J.
      • Laubscher M.
      • Vlutters R.
      • et al.
      An automatic vision-based malaria diagnosis system.
      DAPI/Mitotracker
      • Moon S.
      • Lee S.
      • Kim H.
      • et al.
      An image analysis algorithm for malaria parasite stage classification and viability quantification.
      Toluidine blue
      • Lee S.A.
      • Leitao R.
      • Zheng G.
      • Yang S.
      • Rodriguez A.
      • Yang C.
      Color capable sub-pixel resolving optofluidic microscope and its application to blood cell imaging for malaria diagnosis.
      Unstained
      • Park H.S.
      • Rinehart M.T.
      • Walzer K.A.
      • Chi J.T.A.
      • Wax A.
      Automated detection of P. falciparum using machine learning algorithms with quantitative phase images of unstained cells.
      • Zhang Z.
      • Ong L.S.
      • Fang K.
      • et al.
      Image classification of unlabeled malaria parasites in red blood cells.
      • Bhowmick S.
      • Das D.K.
      • Maiti A.K.
      • Chakraborty C.
      Structural and textural classification of erythrocytes in anaemic cases: a scanning electron microscopic study.
      • Omucheni D.L.
      • Kaduki K.A.
      • Bulimo W.D.
      • Angeyo H.K.
      Application of principal component analysis to multispectral-multimodal optical image analysis for malaria diagnostics.
      ThickGiemsa
      • Wongsrichanalai C.
      • Barcus M.J.
      • Muth S.
      • Sutamihardja A.
      • Wernsdorfer W.H.
      A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT).
      • Elter M.
      • Haßlmeyer E.
      • Zerfaß T.
      Detection of malaria parasites in thick blood films.
      • Toha S.
      • Ngah U.
      Computer aided medical diagnosis for the identification of malaria parasites.
      • Salamah U.
      • Sarno R.
      • Arifin A.
      • et al.
      Enhancement of low quality thick blood smear microscopic images of malaria patients using contrast and edge corrections.
      • Hanif N.
      • Mashor M.
      • Mohamed Z.
      Image enhancement and segmentation using dark stretching technique for Plasmodium falciparum for thick blood smear.
      • Arco J.E.
      • Górriz J.M.
      • Ramírez J.
      • Álvarez I.
      • Puntonet C.G.
      Digital image analysis for automatic enumeration of malaria parasites using morphological operations.
      • Rosado L.
      • da Costa J.M.C.
      • Elias D.
      • Cardoso J.S.
      Automated detection of malaria parasites on thick blood smears via mobile devices.
      • Quinn J.A.
      • Andama A.
      • Munabi I.
      • Kiwanuka F.N.
      Automated blood smear analysis for mobile malaria diagnosis.
      • Herrera S.
      • Vallejo A.F.
      • Quintero J.P.
      • Arévalo-Herrera M.
      • Cancino M.
      • Ferro S.
      Field evaluation of an automated RDT reader and data management device for Plasmodium falciparum/Plasmodium vivax malaria in endemic areas of Colombia.
      • Frean J.A.
      Reliable enumeration of malaria parasites in thick blood films using digital image analysis.
      • Luengo-Oroz M.A.
      • Arranz A.
      • Frean J.
      Crowd sourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears.
      • Yunda L.
      • Ramirez A.A.
      • Millán J.
      Automated image analysis method for p-vivax malaria parasite detection in thick film blood images.
      • Kaewkamnerd S.
      • Uthaipibull C.
      • Intarapanich A.
      • Pannarut M.
      • Chaotheing S.
      • Tongsima S.
      An automatic device for detection and classification of malaria parasite species in thick blood film.
      • Frean J.
      Microscopic determination of malaria parasite load: role of image analysis.
      Leishman
      • Khan N.A.
      • Pervaz H.
      • Latif A.K.
      • et al.
      Unsupervised identification of malaria parasites using computer vision.
      Table I also shows that the majority of approaches, for both thin and thick smears, have adopted the most popular stain in practice, Giemsa. Although stains like Leishman provide very good results for malaria parasites, Giemsa stain has proved to be the best all-round stain for the routine diagnosis of malaria. It has the disadvantage of being relatively expensive, but this is outweighed by its stability over time and its consistent staining quality over a wide range of temperatures.

      Automated Diagnosis of Malaria

      This section provides the core information of our survey, namely a compilation of references that should cover the vast majority of articles ever published on automated microscopy for malaria diagnosis, with the bulk of the articles published in the last 10 years. The work that has been done in this area is quite diverse. Nevertheless, a system for automated cell microscopy usually implements a sequence of key processing steps that can serve as a guideline. Therefore, each of the following subsections will focus on 1 specific aspect of the processing pipeline.
      The first step is usually the acquisition of digital images of blood smears, which largely depends on the equipment and materials being use. The Image acquisition section breaks down the different approaches for the different types of microscopy, blood slides (thin or thick), and staining.
      Following image acquisition, most systems perform one or several preprocessing methods to remove noise and to normalize lighting and color variations inherent in the image acquisition and staining process. The Preprocessing section sorts the publications according to the preprocessing methods implemented.
      The next step usually involves the detection and segmentation (outlining) of individual blood cells and maybe other objects that can be visible in a blood slide image, such as parasites or platelets. The section titled Red blood cell detection and segmentation gives an overview of all the segmentation methods that have been used for microscopic malaria diagnosis.
      For most articles, cell segmentation is followed by the computation of a set of features, which describe the visual appearance of the segmented objects in a mathematical succinct way. The section titled Feature extraction and selection presents the different features and potential feature selection strategies that can be found in the literature.
      In the last step, a mathematical discrimination method that classifies the segmented objects into different classes based on the computed features is implemented. For example, labeling each red blood cell as either infected or uninfected is a key classification task performed in this step, which then allows to compute the parasitemia. The section titled Parasite identification and labeling lists all the classification methods used in the literature for malaria diagnosis.
      Later in the article, in the section titled Deep Learning, we will present references for the latest classification trend, deep learning, which skips the feature computation step and sometimes even the segmentation step. Furthermore, in the section titled Mobile Smatphones for Malaria Diagnosis, we will discuss how smartphones can be used for microscopic malaria diagnosis and list the systems that have already been implemented and published.

       Image acquisition

      Table II lists all published systems according to the type of microscopy used. Because light microscopy is the most common form of malaria diagnosis in resource-poor settings, where automation will also have the largest impact on health care and economy, it is not surprising that most authors implemented systems for standard microscopy. We have also added all other imaging techniques that we found in the literature and for which automated systems have been developed. For more detailed information about these approaches, we refer to the references listed in the table and the reference list at the end of this article.
      • Tangpukdee N.
      • Duangdee C.
      • Wilairatana P.
      • Krudsood S.
      Malaria diagnosis: a brief review.
      • Wongsrichanalai C.
      • Barcus M.J.
      • Muth S.
      • Sutamihardja A.
      • Wernsdorfer W.H.
      A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT).
      • Hänscheid T.
      Diagnosis of malaria: a review of alternatives to conventional microscopy.
      • Parsel S.M.
      • Gustafson S.A.
      • Friedlander E.
      • et al.
      Malaria over-diagnosis in Cameroon: diagnostic accuracy of Fluorescence and Staining Technologies (FAST) Malaria Stain and LED microscopy versus Giemsa and bright field microscopy validated by polymerase chain reaction.
      • Lee S.A.
      • Leitao R.
      • Zheng G.
      • Yang S.
      • Rodriguez A.
      • Yang C.
      Color capable sub-pixel resolving optofluidic microscope and its application to blood cell imaging for malaria diagnosis.
      • Frean J.
      Improving quantification of malaria parasite burden with digital image analysis.
      Table IIMalaria image acquisition
      Imaging techniques
      Light microscopy
      • Mavandadi S.
      • Dimitrov S.
      • Feng S.
      • et al.
      Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.
      • Linder N.
      • Turkki R.
      • Walliander M.
      • et al.
      A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears.
      • Mohammed H.A.
      • Abdelrahman I.A.M.
      Detection and classification of malaria in thin blood slide images.
      • Halim S.
      • Bretschneider T.R.
      • Li Y.
      • Preiser P.R.
      • Kuss C.
      Estimating malaria parasitaemia from blood smear images.
      • Anggraini D.
      • Nugroho A.S.
      • Pratama C.
      • Rozi I.E.
      • Pragesjvara V.
      • Gunawan M.
      Automated status identification of microscopic images obtained from malaria thin blood smears using Bayes decision: a study case in Plasmodium falciparum.
      • Kareem S.
      • Morling R.C.
      • Kale I.
      A novel method to count the red blood cells in thin blood films.
      • Kareem S.
      • Kale I.
      • Morling R.C.
      Automated malaria parasite detection in thin blood films: a hybrid illumination and color constancy insensitive, morphological approach.
      • Nasir A.A.
      • Mashor M.
      • Mohamed Z.
      Segmentation based approach for detection of malaria parasites using moving k-means clustering.
      • Malihi L.
      • Ansari-Asl K.
      • Behbahani A.
      Malaria parasite detection in giemsa-stained blood cell images.
      • Berge H.
      • Taylor D.
      • Krishnan S.
      • Douglas T.S.
      Improved red blood cell counting in thin blood smears.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Automatic thresholding of infected blood images using granulometry and regional extrema.
      • Kareem S.
      • Kale I.
      • Morling R.C.
      Automated P. falciparum detection system for post-treatment malaria diagnosis using modified annular ring ratio method.
      • Zou L.
      • Chen J.
      • Zhang J.
      • Garcia N.
      Malaria cell counting diagnosis within large field of view.
      • Mushabe M.C.
      • Dendere R.
      • Douglas T.S.
      Automated detection of malaria in Giemsa-stained thin blood smears.
      • Savkare S.
      • Narote S.
      Automated system for malaria parasite identification.
      • Mehrjou A.
      • Abbasian T.
      • Izadi M.
      Automatic malaria diagnosis system.
      • Gatc J.
      • Maspiyanti F.
      • Sarwinda D.
      • Arymurthy A.M.
      Plasmodium parasite detection on red blood cell image for the diagnosis of malaria using double thresholding.
      • Nanoti A.
      • Jain S.
      • Gupta C.
      • Vyas G.
      Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear.
      • Maiseli B.
      • Mei J.
      • Gao H.
      • Yin S.
      An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices.
      • Liang Z.
      • Powell A.
      • Ersoy I.
      • et al.
      CNN-based image analysis for malaria diagnosis.
      • Bibin D.
      • Nair M.S.
      • Punitha P.
      Malaria parasite detection from peripheral blood smear images using deep belief networks.
      • Adi K.
      • Pujiyanto S.
      • Gernowo R.
      • Pamungkas A.
      • Putranto A.B.
      Identifying the developmental phase of Plasmodium falciparum in malaria-infected red blood cells using adaptive color segmentation and back propagation neural network.
      • Dallet C.
      • Kareem S.
      • Kale I.
      Real time blood image processing application for malaria diagnosis using mobile phones.
      • Elter M.
      • Haßlmeyer E.
      • Zerfaß T.
      Detection of malaria parasites in thick blood films.
      • Fang Y.
      • Xiong W.
      • Lin W.
      • Chen Z.
      Unsupervised malaria parasite detection based on phase spectrum.
      • May Z.
      • Aziz S.S.A.M.
      • Salamat R.
      Automated quantification and classification of malaria parasites in thin blood smears.
      • Sheikhhosseini M.
      • Rabbani H.
      • Zekri M.
      • Talebi A.
      Automatic diagnosis of malaria based on complete circle–ellipse fitting search algorithm.
      • Díaz G.
      • González F.A.
      • Romero E.
      A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Parasite detection and identification for automated thin blood film malaria diagnosis.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Analysis of infected blood cell images using morphological operators.
      • Sio S.W.
      • Sun W.
      • Kumar S.
      • et al.
      MalariaCount: an image analysis-based program for the accurate determination of parasitemia.
      • Savkare S.
      • Narote S.
      Automatic system for classification of erythrocytes infected with malaria and identification of parasite's life stage.
      • Purwar Y.
      • Shah S.L.
      • Clarke G.
      • Almugairi A.
      • Muehlenbachs A.
      Automated and unsupervised detection of malarial parasites in microscopic images.
      • Ma C.
      • Harrison P.
      • Wang L.
      • Coppel R.L.
      Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears.
      • Proudfoot O.
      • Drew N.
      • Scholzen A.
      • Xiang S.
      • Plebanski M.
      Investigation of a novel approach to scoring Giemsa-stained malaria-infected thin blood films.
      • Kim J.D.
      • Nam K.M.
      • Park C.Y.
      • Kim Y.S.
      • Song H.J.
      Automatic detection of malaria parasite in blood images using two parameters.
      • Abbas N.
      • Saba T.
      • Mohamad D.
      • Rehman A.
      • Almazyad A.S.
      • Al-Ghamdi J.S.
      Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Morphological image processing for evaluating malaria disease.
      • Prasad K.
      • Winter J.
      • Bhat U.M.
      • Acharya R.V.
      • Prabhu G.K.
      Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images.
      • Ghosh P.
      • Bhattacharjee D.
      • Nasipuri M.
      • Basu D.K.
      Medical aid for automatic detection of malaria.
      • Daz G.
      • Gonzalez F.
      • Romero E.
      Infected cell identification in thin blood images based on color pixel classification: comparison and analysis.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Malaria parasite detection in peripheral blood images.
      • Le M.T.
      • Bretschneider T.R.
      • Kuss C.
      • Preiser P.R.
      A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears.
      • Savkare S.
      • Narote S.
      Automatic detection of malaria parasites for estimating parasitemia.
      • Suradkar P.T.
      Detection of malarial parasite in blood using image processing.
      • Aimi Salihah A.N.
      • Yusoff M.
      • Zeehaida M.
      Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering.
      • Khan M.I.
      • Acharya B.
      • Singh B.K.
      • Soni J.
      Content based image retrieval approaches for detection of malarial parasite in blood images.
      • Khatri K.
      • Ratnaparkhe V.
      • Agrawal S.
      • Bhalchandra A.
      Image processing approach for malaria parasite identification.
      • Hirimutugoda Y.
      • Wijayarathna G.
      Image analysis system for detection of red cell disorders using artificial neural networks.
      • Razzak M.I.
      Malarial parasite classification using recurrent neural network.
      • Pamungkas A.
      • Adi K.
      • Gernowo R.
      Identification of Plasmodium falciparum development phase in malaria infected red blood cells using adaptive color segmentation and decision tree based classification.
      • Razzak M.I.
      Automatic detection and classification of malarial parasite.
      • Anand P.R.
      • Bajpai G.
      • Bhaskar V.
      • Job S.M.
      Detection of the malarial parasite infected blood images by 3D-analysis of the cell curved surface.
      • Chavan S.N.
      • Sutkar A.M.
      Malaria disease identification and analysis using image processing.
      • Chayadevi M.
      • Raju G.
      Usage of art for automatic malaria parasite identification based on fractal features.
      • Gitonga L.
      • Memeu D.M.
      • Kaduki K.A.
      • Kale M.A.C.
      • Muriuki N.S.
      Determination of plasmodium parasite life stages and species in images of thin blood smears using artificial neural network.
      • Khan N.A.
      • Pervaz H.
      • Latif A.K.
      • et al.
      Unsupervised identification of malaria parasites using computer vision.
      • Premaratne S.P.
      • Karunaweera N.D.
      • Fernando S.
      • Perera W.S.R.
      • Rajapaksha R.
      A neural network architecture for automated recognition of intracellular malaria parasites in stained blood films.
      • Walliander M.
      • Turkki R.
      • Linder N.
      • et al.
      Automated segmentation of blood cells in Giemsa stained digitized thin blood films.
      • Von Mühlen A.
      Computer image analysis of malarial Plasmodium vivax in human red blood cells.
      • Špringl V.
      Automatic malaria diagnosis through microscopy imaging.
      • Memeu D.M.
      A rapid malaria diagnostic method based on automatic detection and classification of plasmodium parasites in stained thin blood smear images.
      • Das D.
      • Ghosh M.
      • Chakraborty C.
      • Maiti A.K.
      • Pal M.
      Probabilistic prediction of malaria using morphological and textural information.
      • Ghosh M.
      • Das D.
      • Chakraborty C.
      • Ray A.K.
      Plasmodium vivax segmentation using modified fuzzy divergence.
      • Makkapati V.V.
      • Rao R.M.
      Ontology-based malaria parasite stage and species identification from peripheral blood smear images.
      • Das D.
      • Maiti A.
      • Chakraborty C.
      Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.
      • Das D.K.
      • Ghosh M.
      • Pal M.
      • Maiti A.K.
      • Chakraborty C.
      Machine learning approach for automated screening of malaria parasite using light microscopic images.
      • Devi S.S.
      • Sheikh S.A.
      • Talukdar A.
      • Laskar R.H.
      Malaria infected erythrocyte classification based on the histogram features using microscopic images of thin blood smear.
      • Das D.K.
      • Maiti A.K.
      • Chakraborty C.
      Textural pattern classification of microscopic images for malaria screening.
      • Ghosh M.
      • Das D.
      • Chakraborty C.
      • Ray A.K.
      Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach.
      • Kumar A.
      • Choudhary A.
      • Tembhare P.
      • Pote C.
      Enhanced identification of malarial infected objects using Otsu algorithm from thin smear digital images.
      • Maity M.
      • Maity A.K.
      • Dutta P.K.
      • Chakraborty C.
      A web-accessible framework for automated storage with compression and textural classification of malaria parasite images.
      • Panchbhai V.V.
      • Damahe L.B.
      • Nagpure A.V.
      • Chopkar P.N.
      RBCs and parasites segmentation from thin smear blood cell images.
      • Mulay H.D.
      • Murthy T.D.
      • Nerune S.M.
      • Amrutha M.
      New methylene blue stain for malaria detection on thin smears.
      • Dong Y.
      • Jiang Z.
      • Shen H.
      • et al.
      Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells.
      • Muralidharan V.
      • Dong Y.
      • Pan W.D.
      A comparison of feature selection methods for machine learning based automatic malarial cell recognition in wholeslide images.
      • Zhang Z.
      • Ong L.S.
      • Fang K.
      • et al.
      Image classification of unlabeled malaria parasites in red blood cells.
      • Salamah U.
      • Sarno R.
      • Arifin A.
      • et al.
      Enhancement of low quality thick blood smear microscopic images of malaria patients using contrast and edge corrections.
      • Arco J.E.
      • Górriz J.M.
      • Ramírez J.
      • Álvarez I.
      • Puntonet C.G.
      Digital image analysis for automatic enumeration of malaria parasites using morphological operations.
      • Rosado L.
      • da Costa J.M.C.
      • Elias D.
      • Cardoso J.S.
      Automated detection of malaria parasites on thick blood smears via mobile devices.
      • Quinn J.A.
      • Andama A.
      • Munabi I.
      • Kiwanuka F.N.
      Automated blood smear analysis for mobile malaria diagnosis.
      • Frean J.A.
      Reliable enumeration of malaria parasites in thick blood films using digital image analysis.
      • Luengo-Oroz M.A.
      • Arranz A.
      • Frean J.
      Crowd sourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears.
      • Yunda L.
      • Ramirez A.A.
      • Millán J.
      Automated image analysis method for p-vivax malaria parasite detection in thick film blood images.
      • Kaewkamnerd S.
      • Uthaipibull C.
      • Intarapanich A.
      • Pannarut M.
      • Chaotheing S.
      • Tongsima S.
      An automatic device for detection and classification of malaria parasite species in thick blood film.
      • Frean J.
      Microscopic determination of malaria parasite load: role of image analysis.
      • Devi S.S.
      • Roy A.
      • Sharma M.
      • Laskar R.
      kNN classification based erythrocyte separation in microscopic images of thin blood smear.
      • Sharif J.M.
      • Miswan M.
      • Ngadi M.
      • Salam M.S.H.
      • bin Abdul Jamil M.M.
      Red blood cell segmentation using masking and watershed algorithm: a preliminary study.
      • Thung F.
      • Suwardi I.S.
      Blood parasite identification using feature based recognition.
      • Somasekar J.
      • Reddy B.E.
      Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging.
      • Khot S.
      • Prasad R.
      Optimal computer based analysis for detecting malarial parasites.
      • Kumarasamy S.K.
      • Ong S.
      • Tan K.S.
      Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection.
      • Ross N.E.
      • Pritchard C.J.
      • Rubin D.M.
      • Duse A.G.
      Automated image processing method for the diagnosis and classification of malaria on thin blood smears.
      • Somasekar J.
      • Reddy A.R.M.
      • Reddy L.S.
      An efficient algorithm for automatic malaria detection in microscopic blood images.
      • Payne D.
      Use and limitations of light microscopy for diagnosing malaria at the primary health care level.
      • Somasekar J.
      • Reddy B.E.
      • Reddy E.K.
      • Lai C.H.
      An image processing approach for accurate determination of parasitemia in peripheral blood smear images.
      • Memeu D.M.
      • Kaduki K.A.
      • Mjomba A.
      • Muriuki N.S.
      • Gitonga L.
      Detection of plasmodium parasites from images of thin blood smears.
      • Parkhi V.
      • Pawar P.
      • Surve A.
      Computer automation for malaria parasite detection using linear programming.
      Binocolor microscopy
      • Daniel T.
      • Pierre E.
      • Emmanuel T.
      • Philippe B.
      Automated diagnosis of malaria in tropical areas using 40X microscopic images of blood smears.
      • Ahirwar N.
      • Pattnaik S.
      • Acharya B.
      Advanced image analysis based system for automatic detection and classification of malarial parasite in blood images.
      • Soni J.
      Advanced image analysis based system for automatic detection of malarial parasite in blood images using SUSAN approach.
      • Soni J.
      • Mishra N.
      • Kamargaonkar N.
      Automatic difference between RBC and malaria parasites based on morphology with first order features using image processing.
      Fluorescent microscopy
      • Parsel S.M.
      • Gustafson S.A.
      • Friedlander E.
      • et al.
      Malaria over-diagnosis in Cameroon: diagnostic accuracy of Fluorescence and Staining Technologies (FAST) Malaria Stain and LED microscopy versus Giemsa and bright field microscopy validated by polymerase chain reaction.
      • Shute G.
      • Sodeman T.
      Identification of malaria parasites by fluorescence microscopy and acridine orange staining.
      • Kawamoto F.
      Rapid diagnosis of malaria by fluorescence microscopy with light microscope and interference filter.
      • Wongsrichanalai C.
      • Kawamotob F.
      Fluorescent microscopy and fluorescent labelling for malaria diagnosis.
      • Guy R.
      • Liu P.
      • Pennefather P.
      • Crandall I.
      The use of fluorescence enhancement to improve the microscopic diagnosis of falciparum malaria.
      • Breslauer D.N.
      • Maamari R.N.
      • Switz N.A.
      • Lam W.A.
      • Fletcher D.A.
      Mobile phone based clinical microscopy for global health applications.
      • Moon S.
      • Lee S.
      • Kim H.
      • et al.
      An image analysis algorithm for malaria parasite stage classification and viability quantification.
      Polarized microscopy
      • Pirnstill C.W.
      • Coté G.L.
      Malaria diagnosis using a mobile phone polarized microscope.
      Multi-spectral and multi-modal microscopy
      • Omucheni D.L.
      • Kaduki K.A.
      • Bulimo W.D.
      • Angeyo H.K.
      Application of principal component analysis to multispectral-multimodal optical image analysis for malaria diagnostics.
      • DABO-NIANG S.
      • Zoueu J.
      Combining kriging, multispectral and multimodal microscopy to resolve malaria-infected erythrocyte contents.
      Image-based cytometer
      • Yang D.
      • Subramanian G.
      • Duan J.
      • et al.
      A portable image-based cytometer for rapid malaria detection and quantification.
      Sub-pixel resolving optofluidic microscopy (SROFM)
      • Lee S.A.
      • Leitao R.
      • Zheng G.
      • Yang S.
      • Rodriguez A.
      • Yang C.
      Color capable sub-pixel resolving optofluidic microscope and its application to blood cell imaging for malaria diagnosis.
      Quantitative phase imaging (QPI)
      • Park H.S.
      • Rinehart M.T.
      • Walzer K.A.
      • Chi J.T.A.
      • Wax A.
      Automated detection of P. falciparum using machine learning algorithms with quantitative phase images of unstained cells.
      Quantitative cartridge-scanner system
      • Vink J.
      • Laubscher M.
      • Vlutters R.
      • et al.
      An automatic vision-based malaria diagnosis system.
      Scanning electron microscopy (SEM)
      • Bhowmick S.
      • Das D.K.
      • Maiti A.K.
      • Chakraborty C.
      Structural and textural classification of erythrocytes in anaemic cases: a scanning electron microscopic study.
      Fiber array-based Raman imaging
      • Brückner M.
      • Becker K.
      • Popp J.
      • Frosch T.
      Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells.
      • Chen F.
      • Flaherty B.R.
      • Cohen C.E.
      • Peterson D.S.
      • Zhao Y.
      Direct detection of malaria infected red blood cells by surface enhanced Raman spectroscopy.
      Serial block-face scanning electron microscopy (SBFSEM)
      • Sakaguchi M.
      • Miyazaki N.
      • Fujioka H.
      • Kaneko O.
      • Murata K.
      Three-dimensional analysis of morphological changes in the malaria parasite infected red blood cell by serial block-face scanning electron microscopy.
      SightDx digital imaging scanning
      • Srivastava B.
      • Anvikar A.R.
      • Ghosh S.K.
      • et al.
      Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria.

       Preprocessing

      Table III lists all preprocessing approaches that have been applied to automatic analysis of digital blood slide images.
      Table IIIImage preprocessing techniques applied to enhance malaria blood smear images
      Blood smearChallengesPreprocessing methodsRemarks
      ThinNoise reductionMean filtering
      • Ajala F.
      • Fenwa O.
      • Aku M.
      Comparative analysis of different types of malaria diseases using first order features.
      • Tulsani H.
      • Saxena S.
      • Yadav N.
      Segmentation using morphological watershed transformation for counting blood cells.
      Median filtering
      • Yang D.
      • Subramanian G.
      • Duan J.
      • et al.
      A portable image-based cytometer for rapid malaria detection and quantification.
      • Linder N.
      • Turkki R.
      • Walliander M.
      • et al.
      A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears.
      • Di Rubeto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Segmentation of blood images using morphological operators.
      • Anggraini D.
      • Nugroho A.S.
      • Pratama C.
      • Rozi I.E.
      • Pragesjvara V.
      • Gunawan M.
      Automated status identification of microscopic images obtained from malaria thin blood smears using Bayes decision: a study case in Plasmodium falciparum.
      • Nasir A.A.
      • Mashor M.
      • Mohamed Z.
      Segmentation based approach for detection of malaria parasites using moving k-means clustering.
      • Malihi L.
      • Ansari-Asl K.
      • Behbahani A.
      Malaria parasite detection in giemsa-stained blood cell images.
      • Berge H.
      • Taylor D.
      • Krishnan S.
      • Douglas T.S.
      Improved red blood cell counting in thin blood smears.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Automatic thresholding of infected blood images using granulometry and regional extrema.
      • Mushabe M.C.
      • Dendere R.
      • Douglas T.S.
      Automated detection of malaria in Giemsa-stained thin blood smears.
      • Savkare S.
      • Narote S.
      Automated system for malaria parasite identification.
      • Mehrjou A.
      • Abbasian T.
      • Izadi M.
      Automatic malaria diagnosis system.
      • Gatc J.
      • Maspiyanti F.
      • Sarwinda D.
      • Arymurthy A.M.
      Plasmodium parasite detection on red blood cell image for the diagnosis of malaria using double thresholding.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Analysis of infected blood cell images using morphological operators.
      • Savkare S.
      • Narote S.
      Automatic system for classification of erythrocytes infected with malaria and identification of parasite's life stage.
      • Daniel T.
      • Pierre E.
      • Emmanuel T.
      • Philippe B.
      Automated diagnosis of malaria in tropical areas using 40X microscopic images of blood smears.
      • Abbas N.
      • Saba T.
      • Mohamad D.
      • Rehman A.
      • Almazyad A.S.
      • Al-Ghamdi J.S.
      Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears.
      • Razzak M.I.
      Malarial parasite classification using recurrent neural network.
      • Walliander M.
      • Turkki R.
      • Linder N.
      • et al.
      Automated segmentation of blood cells in Giemsa stained digitized thin blood films.
      • Das D.
      • Ghosh M.
      • Chakraborty C.
      • Maiti A.K.
      • Pal M.
      Probabilistic prediction of malaria using morphological and textural information.
      • Devi S.S.
      • Sheikh S.A.
      • Talukdar A.
      • Laskar R.H.
      Malaria infected erythrocyte classification based on the histogram features using microscopic images of thin blood smear.
      • Das D.K.
      • Maiti A.K.
      • Chakraborty C.
      Textural pattern classification of microscopic images for malaria screening.
      • Ghosh M.
      • Das D.
      • Chakraborty C.
      • Ray A.K.
      Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach.
      • Maity M.
      • Maity A.K.
      • Dutta P.K.
      • Chakraborty C.
      A web-accessible framework for automated storage with compression and textural classification of malaria parasite images.
      • Somasekar J.
      • Reddy B.E.
      Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging.
      Remove impulse noise and preserve edges
      Geometric mean filtering
      • Das D.K.
      • Ghosh M.
      • Pal M.
      • Maiti A.K.
      • Chakraborty C.
      Machine learning approach for automated screening of malaria parasite using light microscopic images.
      • Das D.
      • Chakraborty C.
      • Mitra B.
      • Maiti A.
      • Ray A.
      Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia.
      Wiener filtering
      • May Z.
      • Aziz S.S.A.M.
      • Salamat R.
      Automated quantification and classification of malaria parasites in thin blood smears.
      Gamma equalization
      • Somasekar J.
      • Reddy B.E.
      Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging.
      SUSAN nonlinear filtering
      • Ahirwar N.
      • Pattnaik S.
      • Acharya B.
      Advanced image analysis based system for automatic detection and classification of malarial parasite in blood images.
      • Soni J.
      Advanced image analysis based system for automatic detection of malarial parasite in blood images using SUSAN approach.
      Gaussian low-pass filtering
      • Ma C.
      • Harrison P.
      • Wang L.
      • Coppel R.L.
      Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears.
      • Chayadevi M.
      • Raju G.
      Usage of art for automatic malaria parasite identification based on fractal features.
      • Arco J.E.
      • Górriz J.M.
      • Ramírez J.
      • Álvarez I.
      • Puntonet C.G.
      Digital image analysis for automatic enumeration of malaria parasites using morphological operations.
      Nonlinear diffusion filtering
      • Sheikhhosseini M.
      • Rabbani H.
      • Zekri M.
      • Talebi A.
      Automatic diagnosis of malaria based on complete circle–ellipse fitting search algorithm.
      Gamma transformation
      • Skandarajah A.
      • Reber C.D.
      • Switz N.A.
      • Fletcher D.A.
      Quantitative imaging with a mobile phone microscope.
      Interscale orthogonal wavelet-based thresholding
      • Mandal S.
      • Kumar A.
      • Chatterjee J.
      • Manjunatha M.
      • Ray A.K.
      Segmentation of blood smear images using normalized cuts for detection of malarial parasites.
      Perona-Malik denoising model
      • Maiseli B.
      • Mei J.
      • Gao H.
      • Yin S.
      An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices.
      Morphological operations
      • Anggraini D.
      • Nugroho A.S.
      • Pratama C.
      • Rozi I.E.
      • Pragesjvara V.
      • Gunawan M.
      Automated status identification of microscopic images obtained from malaria thin blood smears using Bayes decision: a study case in Plasmodium falciparum.
      • Malihi L.
      • Ansari-Asl K.
      • Behbahani A.
      Malaria parasite detection in giemsa-stained blood cell images.
      • Berge H.
      • Taylor D.
      • Krishnan S.
      • Douglas T.S.
      Improved red blood cell counting in thin blood smears.
      • Mushabe M.C.
      • Dendere R.
      • Douglas T.S.
      Automated detection of malaria in Giemsa-stained thin blood smears.
      • Dallet C.
      • Kareem S.
      • Kale I.
      Real time blood image processing application for malaria diagnosis using mobile phones.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Parasite detection and identification for automated thin blood film malaria diagnosis.
      • Savkare S.
      • Narote S.
      Automatic detection of malaria parasites for estimating parasitemia.
      • Khan M.I.
      • Acharya B.
      • Singh B.K.
      • Soni J.
      Content based image retrieval approaches for detection of malarial parasite in blood images.
      • Razzak M.I.
      Automatic detection and classification of malarial parasite.
      • Von Mühlen A.
      Computer image analysis of malarial Plasmodium vivax in human red blood cells.
      • Damahe L.B.
      • Krishna R.
      • Janwe N.
      Segmentation based approach to detect parasites and RBCs in blood cell images.
      • Maity M.
      • Maity A.K.
      • Dutta P.K.
      • Chakraborty C.
      A web-accessible framework for automated storage with compression and textural classification of malaria parasite images.
      • Dong Y.
      • Jiang Z.
      • Shen H.
      • et al.
      Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells.
      • Somasekar J.
      • Reddy B.E.
      • Reddy E.K.
      • Lai C.H.
      An image processing approach for accurate determination of parasitemia in peripheral blood smear images.
      Remove unwanted small objects, hole filling, closing and opening
      Low image contrastLaplacian filtering
      • Savkare S.
      • Narote S.
      Automated system for malaria parasite identification.
      • Savkare S.
      • Narote S.
      Automatic system for classification of erythrocytes infected with malaria and identification of parasite's life stage.
      • Ghosh P.
      • Bhattacharjee D.
      • Nasipuri M.
      • Basu D.K.
      Medical aid for automatic detection of malaria.
      Edge detection
      Adaptive/local histogram equalization
      • Savkare S.
      • Narote S.
      Automated system for malaria parasite identification.
      • Mehrjou A.
      • Abbasian T.
      • Izadi M.
      Automatic malaria diagnosis system.
      • Maiseli B.
      • Mei J.
      • Gao H.
      • Yin S.
      An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices.
      • Sio S.W.
      • Sun W.
      • Kumar S.
      • et al.
      MalariaCount: an image analysis-based program for the accurate determination of parasitemia.
      • Purwar Y.
      • Shah S.L.
      • Clarke G.
      • Almugairi A.
      • Muehlenbachs A.
      Automated and unsupervised detection of malarial parasites in microscopic images.
      • Suradkar P.T.
      Detection of malarial parasite in blood using image processing.
      • Razzak M.I.
      Malarial parasite classification using recurrent neural network.
      • Salamah U.
      • Sarno R.
      • Arifin A.
      • et al.
      Enhancement of low quality thick blood smear microscopic images of malaria patients using contrast and edge corrections.
      • Arco J.E.
      • Górriz J.M.
      • Ramírez J.
      • Álvarez I.
      • Puntonet C.G.
      Digital image analysis for automatic enumeration of malaria parasites using morphological operations.
      • Maitra M.
      • Gupta R.K.
      • Mukherjee M.
      Detection and counting of red blood cells in blood cell images using Hough transform.
      Enhance image resolution
      Forward discrete curvelet transform
      • Razzak M.I.
      Malarial parasite classification using recurrent neural network.
      Contrast stretching techniques
      • Nasir A.A.
      • Mashor M.
      • Mohamed Z.
      Segmentation based approach for detection of malaria parasites using moving k-means clustering.
      • Nanoti A.
      • Jain S.
      • Gupta C.
      • Vyas G.
      Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear.
      • Maity M.
      • Maity A.K.
      • Dutta P.K.
      • Chakraborty C.
      A web-accessible framework for automated storage with compression and textural classification of malaria parasite images.
      • Salamah U.
      • Sarno R.
      • Arifin A.
      • et al.
      Enhancement of low quality thick blood smear microscopic images of malaria patients using contrast and edge corrections.
      • Hanif N.
      • Mashor M.
      • Mohamed Z.
      Image enhancement and segmentation using dark stretching technique for Plasmodium falciparum for thick blood smear.
      Contrast enhancement
      Uneven illuminationLow-pass filtering
      • Díaz G.
      • González F.A.
      • Romero E.
      A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Parasite detection and identification for automated thin blood film malaria diagnosis.
      • Daz G.
      • Gonzalez F.
      • Romero E.
      Infected cell identification in thin blood images based on color pixel classification: comparison and analysis.
      • Arco J.E.
      • Górriz J.M.
      • Ramírez J.
      • Álvarez I.
      • Puntonet C.G.
      Digital image analysis for automatic enumeration of malaria parasites using morphological operations.
      Remove high frequency components
      Morphological top-hat operation
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Parasite detection and identification for automated thin blood film malaria diagnosis.
      • Razzak M.I.
      Automatic detection and classification of malarial parasite.
      • Von Mühlen A.
      Computer image analysis of malarial Plasmodium vivax in human red blood cells.
      Remove nonuniform illumination effects
      Cell staining variationLinear model
      • Halim S.
      • Bretschneider T.R.
      • Li Y.
      • Preiser P.R.
      • Kuss C.
      Estimating malaria parasitaemia from blood smear images.
      Color normalization
      • Khatri K.
      • Ratnaparkhe V.
      • Agrawal S.
      • Bhalchandra A.
      Image processing approach for malaria parasite identification.
      Illumination correction
      Gray world color normalization
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      A colour normalization method for giemsa-stained blood cell images.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Malaria parasite detection in peripheral blood images.
      • Hirimutugoda Y.
      • Wijayarathna G.
      Image analysis system for detection of red cell disorders using artificial neural networks.
      • Cesario M.
      • Lundon M.
      • Luz S.
      • Masoodian M.
      • Rogers B.
      Mobile support for diagnosis of communicable diseases in remote locations.
      • Das D.K.
      • Ghosh M.
      • Pal M.
      • Maiti A.K.
      • Chakraborty C.
      Machine learning approach for automated screening of malaria parasite using light microscopic images.
      • Devi S.S.
      • Sheikh S.A.
      • Talukdar A.
      • Laskar R.H.
      Malaria infected erythrocyte classification based on the histogram features using microscopic images of thin blood smear.
      • Das D.K.
      • Maiti A.K.
      • Chakraborty C.
      Textural pattern classification of microscopic images for malaria screening.
      • Das D.
      • Chakraborty C.
      • Mitra B.
      • Maiti A.
      • Ray A.
      Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia.
      Normalization of image color profile
      ThickNoise reductionMedian filtering
      • Rosado L.
      • da Costa J.M.C.
      • Elias D.
      • Cardoso J.S.
      Automated detection of malaria parasites on thick blood smears via mobile devices.
      • Frean J.A.
      Reliable enumeration of malaria parasites in thick blood films using digital image analysis.
      Contrast enhancement
      • Salamah U.
      • Sarno R.
      • Arifin A.
      • et al.
      Enhancement of low quality thick blood smear microscopic images of malaria patients using contrast and edge corrections.
      • Hanif N.
      • Mashor M.
      • Mohamed Z.
      Image enhancement and segmentation using dark stretching technique for Plasmodium falciparum for thick blood smear.
      Gaussian low-pass filter
      • Brückner M.
      • Becker K.
      • Popp J.
      • Frosch T.
      Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells.
      Histogram Equalization
      • Brückner M.
      • Becker K.
      • Popp J.
      • Frosch T.
      Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells.
      Laplacian spatial filter
      • Kaewkamnerd S.
      • Uthaipibull C.
      • Intarapanich A.
      • Pannarut M.
      • Chaotheing S.
      • Tongsima S.
      An automatic device for detection and classification of malaria parasite species in thick blood film.
      Preprocessing is mainly applied to improve the quality of the image and to reduce variations in the images that would unnecessarily complicate the subsequent processing steps. Three key objectives can be identified: noise removal, contrast improvement, illumination and staining correction.
      For noise removal, the most popular approaches have been well-established filters, such as mean and median filters, or Gaussian low-pass filtering. In addition, applying morphologic operations is very popular. For contrast improvement, contrast stretching techniques and histogram equalization in particular, have been the most popular approaches. For illumination and staining variations, color normalization techniques have been applied, including the popular use of grayscale colors.

       Red blood cell detection and segmentation

      Table IV shows the different segmentation techniques applied to thin smears. The vast majority of these techniques are thresholding techniques, such as Otsu thresholding in combination with morphologic operations. However, these techniques may not be dominating because of their superior performance compared with other methods, but rather because of their relative simplicity. Other methods include Hough transform, which makes assumptions about the blood cell shape, and unsupervised k-means pixel clustering. Cell segmentation needs to be accurate to compute the correct parasitemia. However, touching cells in particular complicate the identification and segmentation of individual cells. For this problem, methods like watershed and active contours have been applied.
      Table IVSegmentation techniques for thin blood smears
      Blood smearSegmentation techniquesRemarks
      ThinOtsu thresholding
      • Anggraini D.
      • Nugroho A.S.
      • Pratama C.
      • Rozi I.E.
      • Pragesjvara V.
      • Gunawan M.
      Automated status identification of microscopic images obtained from malaria thin blood smears using Bayes decision: a study case in Plasmodium falciparum.
      • Malihi L.
      • Ansari-Asl K.
      • Behbahani A.
      Malaria parasite detection in giemsa-stained blood cell images.
      • Savkare S.
      • Narote S.
      Automated system for malaria parasite identification.
      • Mehrjou A.
      • Abbasian T.
      • Izadi M.
      Automatic malaria diagnosis system.
      • Gatc J.
      • Maspiyanti F.
      • Sarwinda D.
      • Arymurthy A.M.
      Plasmodium parasite detection on red blood cell image for the diagnosis of malaria using double thresholding.
      • May Z.
      • Aziz S.S.A.M.
      • Salamat R.
      Automated quantification and classification of malaria parasites in thin blood smears.
      • Savkare S.
      • Narote S.
      Automatic system for classification of erythrocytes infected with malaria and identification of parasite's life stage.
      • Savkare S.
      • Narote S.
      Automatic detection of malaria parasites for estimating parasitemia.
      • Walliander M.
      • Turkki R.
      • Linder N.
      • et al.
      Automated segmentation of blood cells in Giemsa stained digitized thin blood films.
      • Von Mühlen A.
      Computer image analysis of malarial Plasmodium vivax in human red blood cells.
      • Das D.
      • Ghosh M.
      • Chakraborty C.
      • Maiti A.K.
      • Pal M.
      Probabilistic prediction of malaria using morphological and textural information.
      • Kumar A.
      • Choudhary A.
      • Tembhare P.
      • Pote C.
      Enhanced identification of malarial infected objects using Otsu algorithm from thin smear digital images.
      • Maity M.
      • Maity A.K.
      • Dutta P.K.
      • Chakraborty C.
      A web-accessible framework for automated storage with compression and textural classification of malaria parasite images.
      • Panchbhai V.V.
      • Damahe L.B.
      • Nagpure A.V.
      • Chopkar P.N.
      RBCs and parasites segmentation from thin smear blood cell images.
      • Moon S.
      • Lee S.
      • Kim H.
      • et al.
      An image analysis algorithm for malaria parasite stage classification and viability quantification.
      • Devi S.S.
      • Roy A.
      • Sharma M.
      • Laskar R.
      kNN classification based erythrocyte separation in microscopic images of thin blood smear.
      • Gopakumar G.P.
      • Swetha M.
      • Sai Siva G.
      • Subrahmanyam S.
      Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner.
      Calculates optimum threshold assuming that image contains bimodal histogram
      (Adaptive) histogram thresholding
      • Yang D.
      • Subramanian G.
      • Duan J.
      • et al.
      A portable image-based cytometer for rapid malaria detection and quantification.
      • Halim S.
      • Bretschneider T.R.
      • Li Y.
      • Preiser P.R.
      • Kuss C.
      Estimating malaria parasitaemia from blood smear images.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Automatic thresholding of infected blood images using granulometry and regional extrema.
      • Zou L.
      • Chen J.
      • Zhang J.
      • Garcia N.
      Malaria cell counting diagnosis within large field of view.
      • Maiseli B.
      • Mei J.
      • Gao H.
      • Yin S.
      An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices.
      • Adi K.
      • Pujiyanto S.
      • Gernowo R.
      • Pamungkas A.
      • Putranto A.B.
      Identifying the developmental phase of Plasmodium falciparum in malaria-infected red blood cells using adaptive color segmentation and back propagation neural network.
      • Daniel T.
      • Pierre E.
      • Emmanuel T.
      • Philippe B.
      Automated diagnosis of malaria in tropical areas using 40X microscopic images of blood smears.
      • Prasad K.
      • Winter J.
      • Bhat U.M.
      • Acharya R.V.
      • Prabhu G.K.
      Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images.
      • Pamungkas A.
      • Adi K.
      • Gernowo R.
      Identification of Plasmodium falciparum development phase in malaria infected red blood cells using adaptive color segmentation and decision tree based classification.
      • Ghate D.A.
      • Jadhav C.
      Automatic detection of malaria parasite from blood images.
      • Makkapati V.V.
      • Rao R.M.
      Segmentation of malaria parasites in peripheral blood smear images.
      • Dong Y.
      • Jiang Z.
      • Shen H.
      • et al.
      Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells.
      • Muralidharan V.
      • Dong Y.
      • Pan W.D.
      A comparison of feature selection methods for machine learning based automatic malarial cell recognition in wholeslide images.
      • Breslauer D.N.
      • Maamari R.N.
      • Switz N.A.
      • Lam W.A.
      • Fletcher D.A.
      Mobile phone based clinical microscopy for global health applications.
      • Park H.S.
      • Rinehart M.T.
      • Walzer K.A.
      • Chi J.T.A.
      • Wax A.
      Automated detection of P. falciparum using machine learning algorithms with quantitative phase images of unstained cells.
      • Zhang Z.
      • Ong L.S.
      • Fang K.
      • et al.
      Image classification of unlabeled malaria parasites in red blood cells.
      • Bhowmick S.
      • Das D.K.
      • Maiti A.K.
      • Chakraborty C.
      Structural and textural classification of erythrocytes in anaemic cases: a scanning electron microscopic study.
      • Frean J.A.
      Reliable enumeration of malaria parasites in thick blood films using digital image analysis.
      • Ross N.E.
      • Pritchard C.J.
      • Rubin D.M.
      • Duse A.G.
      Automated image processing method for the diagnosis and classification of malaria on thin blood smears.
      Difficult to determine the thresholding value
      Zack thresholding
      • Damahe L.B.
      • Krishna R.
      • Janwe N.
      Segmentation based approach to detect parasites and RBCs in blood cell images.
      Triangle-based method particularly effective with a weak peak in the image histogram
      Poisson distribution thresholding
      • Suryawanshi M.S.
      • Dixit V.
      Improved technique for detection of malaria parasites within the blood cell images.
      Finding a threshold that separates foreground and background using minimum error
      Morphological operation
      • Mohammed H.A.
      • Abdelrahman I.A.M.
      Detection and classification of malaria in thin blood slide images.
      • Di Rubeto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Segmentation of blood images using morphological operators.
      • Kareem S.
      • Morling R.C.
      • Kale I.
      A novel method to count the red blood cells in thin blood films.
      • Kareem S.
      • Kale I.
      • Morling R.C.
      Automated malaria parasite detection in thin blood films: a hybrid illumination and color constancy insensitive, morphological approach.
      • Berge H.
      • Taylor D.
      • Krishnan S.
      • Douglas T.S.
      Improved red blood cell counting in thin blood smears.
      • Kareem S.
      • Kale I.
      • Morling R.C.
      Automated P. falciparum detection system for post-treatment malaria diagnosis using modified annular ring ratio method.
      • Mushabe M.C.
      • Dendere R.
      • Douglas T.S.
      Automated detection of malaria in Giemsa-stained thin blood smears.
      • Tek F.B.
      • Dempster A.G.
      • Kale I.
      Parasite detection and identification for automated thin blood film malaria diagnosis.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Analysis of infected blood cell images using morphological operators.
      • Di Ruberto C.
      • Dempster A.
      • Khan S.
      • Jarra B.
      Morphological image processing for evaluating malaria disease.
      • Khan M.I.
      • Acharya B.
      • Singh B.K.
      • Soni J.
      Content based image retrieval approaches for detection of malarial parasite in blood images.
      • Khatri K.
      • Ratnaparkhe V.
      • Agrawal S.
      • Bhalchandra A.
      Image processing approach for malaria parasite identification.
      • Razzak M.I.
      Malarial parasite classification using recurrent neural network.
      • Razzak M.I.
      Automatic detection and classification of malarial parasite.
      • Soni J.
      • Mishra N.
      • Kamargaonkar N.
      Automatic difference between RBC and malaria parasites based on morphology with first order features using image processing.
      • Arco J.E.
      • Górriz J.M.
      • Ramírez J.
      • Álvarez I.
      • Puntonet C.G.
      Digital image analysis for automatic enumeration of malaria parasites using morphological operations.