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Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear

Malaria is responsible for nearly 438,000 deaths worldwide in a year. A total of 214 million cases of malaria are encountered annually. The conventional method for testing malaria is through microscopy. A blood sample of the patient is spread over a glass slide, stained with Giemsa stain and examined under a microscope. It takes a few hours and a highly trained professional to visually examine the slide and give the results. It is even more difficult to detect the different types of malaria parasite and their stages by the conventional methods. The proposed method involves acquisition of the thin blood smear microscopic image at 100x magnification, pre-processing by partial contrast stretching, separation of infected cell from the image by applying k-means clustering on the a∗b component of L∗a∗b color space, feature extraction (shape and textural) of the infected cell, feature reduction using one way ANOVA and finally training the K-nearest neighbor classifier to test the images. Instead of extracting features for the entire group of erythrocytes present in the image, the algorithm only processes the infected cells increasing the speed, effectiveness and efficiency of testing. The KNN classifier is trained with 300 images to detect three lifecycle stages (trophozite, schizont and gametocyte) for each of the four species of malarial parasites (P.falciparum, P.vivax, P.malariae, and P.ovale) with an accuracy of 90.17% and sensitivity of 90.23%.