Kaushik Chakrabortya ,Arnab Chattopadhyayb ,Amlan Chakrabarti ,Tinku Acharyad ,Anjan Kr Dasguptae *
Abstract Thick blood smear examination is a necessary part for rapid screening of malaria parasite. Primary diagnosis of malaria by thick smear examination is cheap and highly sensitive, advocated by the World Health Organization (WHO). For the examination of thick blood smear, manpower and time can be reduced by using automated computational techniques. These techniques would facilitate such detection that are usually based on morphology and in few cases on analysis of colors. In this work, we propose a combined algorithm consisting of morphological operations and color based pixel discrimination technique to identify malaria parasites from thick smear images of Plasmodium vivax. Using morphological operation segmentation of cells from thick blood smear image is done and color based pixel discriminator distinguished malaria cells from segmented image. Evaluation of percentage of detection and False Positive Rate (FPR) shows that our proposed algorithm has significantly higher predictive rate, and lower FPR as compared to any existing methodology when tested on same input slides. Importantly the reported method does not need any training set and assumes unsupervised methodology. This makes the portability of the approach at various scales as discussed in the paper. The experiment has been carried out using JSB (Jaswant Singh Battacharya) stained thick blood smear images. It is assuming that our algorithm can identify malaria in less perfect dirty slides. This makes the algorithm more powerful and robust.
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Journal of Health & Medical Informatics received 2128 citations as per Google Scholar report
