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In this work an image processing system was developed to identify malaria parasites in thin blood smears and to classify them into one of the four different species of malaria. Many techniques were implemented in the preprocessing stage to enhance the images. In the first part of the system morphological processing is applied to extract the Red Blood Cells (RBC) from blood images. The developed algorithm picks the suspicious regions and detects the parasites in the images including the overlapped cells. Accordingly, the RBCs are classified into infected and non-infected cells and the number of RBCs in each image is calculated. The second part of the system uses the Normalized Cross-Correlation function to classify the parasite into one of the four species namely, Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale. Compared to manual results, the system achieved 95 % accuracy for detection and counting of RBCs and 100% for detection and classifying the parasite into one of its four types.

The mathematical morphology technique, granulometry, is used to create the pattern spectrum of a malarial blood slide image. From this, the red blood cell size can be evaluated, making subsequent processing scale-invariant.