Automatic classification of plasmodium parasites using stained RGB images

Abstract

In this work, an accurate, speedy and affordable model of malaria diagnosis using stained thin blood smear images as developed. The method makes use of the morphological, colour and texture features of plasmodium parasites and erythrocytes. Images of infected erythrocytes were acquired, pre-processed and relevant features extracted from them. Image preprocessing entailed reducing the size of the acquired images to speed up processing and median filtering to remove salt and paper noise. Neural network classifiers were then trained and used to detect and determine the life stages and species of plasmodium parasites. Template matching technique was used to approximate the number of erythrocytes in the images and hence estimate the degree of infection (parasitemia).