Parasite detection and identification for automated thin blood film malaria diagnosis

Tek, F. Boray; Dempster, Andrew G.; Kale, I.

doi:10.1016/j.cviu.2009.08.003

Cited by 176 publications

(111 citation statements)

References 33 publications

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“…When it comes to pre-processing, illumination correction has been implemented by Diaz et al [31], Tek et al [15]. A Kruskal Wallis H test performed on a randomly selected set of three images revealed that there is significant difference in luminance distribution in the images, χ2(2)=21.706, p=0.000<0.01.…”

Section: Limitationsmentioning

confidence: 99%

“…Author Halim et al [43], performed parasite detection using a Variance based approach and separately a Colour Based Co-occurrence Matrix based matching technique. Authors Tek et al [15] used RGB histogram and probability density function to determine parasite region. Toha & Ngah [10], calculated a threshold value to identify parasite region followed by calculating the Euclidean distance to differentiate between each parasite cluster.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tek et al [15], used K-NN classifier with 20 classes to identify four species and four stage for each species and normal cases. Ross et al [17], used geometric and texture features with Back Propagation Neural Network for classification of parasite infected red blood cell.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

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Section: Limitationsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

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“…According to the World Health Organization, it causes more than 1 million deaths arising from approximately 300 to 500 million infections every year [6]. It is a serious global health problem and rapid, precise determination of parasitemia is necessary for malaria research and in clinical settings.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of different types of Malaria Diseases using First Order Features

Ajala¹,

Funmilola²,

Fenwa³

et al. 2015

IJAIS

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Malaria is majorly caused by three parasitic organisms namely, P. malaria, P. vivax, P. falciparum. Physician (Microbiologists, Laboratory technicians, Medical Practitioners, and medical experts) examines erythrocytes under light microscope to study the color and morphological changes toward malaria diagnosis. Assessment accuracy depends on the physician-pathological understanding. To help physicians in cases where they may be wrong, developing a computer assisted malaria parasite detection tool has helped modern pathological services where a physician is able to get assistance in order to quickly make better decision toward malaria diagnosis. Segmentation of medical images helps in identifying features that are needed for clinical diagnosis. First order features always give accurate information about the medical image and useful for identification and analysis. In this work, first order features which entails the mean, median, mode, standard deviation, energy, Skewness, kurtosis, area, entropy were extracted from the infected malaria images. The corresponding results show a significant steady value which could be useful during classification of medical images. The work is ongoing, the aim of the work is to classify the malaria parasite and to come up with a diagnostic system that will be able to diagnose tissue and blood cell based diseases like malaria, Ebola etc.

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“…Furthermore, manual counting from Giemsa-smears is known to vary depending on the personnel engaged and quality of smears involving a high chance of misinterpreting other microorganisms such as bacteria and fungi as Plasmodium parasites and difficulties associated with identifying different Plasmodium strains [31]. Previously, several laboratories explored image processing based automated cell counting for parasitemia estimation, but most of them suffered from high false positive values [32][33][34], inferior accuracy [35], inability to differentiate parasitic stages [36] primarily rings [37] and the requirement of fluorescent dyes [38,39].…”

Section: Introductionmentioning

confidence: 99%

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

Yang

Subramanian

Duan

et al. 2017

Proceedings of the 7th International Multidisciplinary Conference on Optofluidics 2017

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Increasing resistance by malaria parasites to currently used antimalarials across the developing world warrants timely detection and classification so that appropriate drug combinations can be administered before clinical complications arise. However, this is often challenged by low levels of infection (referred to as parasitemia) and presence of predominantly young parasitic forms in the patients' peripheral blood. Herein, we developed a simple, inexpensive and portable image-based cytometer that detects and numerically counts Plasmodium falciparum infected red blood cells (iRBCs) from Giemsa-stained smears derived from infected blood. Our cytometer is able to classify all parasitic subpopulations by quantifying the area occupied by the parasites within iRBCs, with high specificity, sensitivity and negligible false positives (~0.0025%). Moreover, we demonstrate the application of our image-based cytometer in testing anti-malarial efficacy against a commercial flow cytometer and demonstrate comparable results between the two methods. Collectively, these results highlight the possibility to use our image-based cytometer as a cheap, rapid and accurate alternative for antimalarial testing without compromising on efficiency and minimal processing time. With appropriate filters applied into the algorithm, to rule out leukocytes and reticulocytes, our cytometer may also be used for field diagnosis of malaria.

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Parasite detection and identification for automated thin blood film malaria diagnosis

Cited by 176 publications

References 33 publications

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Comparative Analysis of different types of Malaria Diseases using First Order Features

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

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