| dc.description.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). | en |
