A Review on Detection and Classification Malaria in Thin Blood Slide Images

Abstract

An image processing system was developed for identifying malaria parasite. There are four type of malaria parasite present in the world. Lots of advanced techniques were implemented in the preprocessing stage to enhance the images. In the system morphological processing is applied to extract the Red Blood Cells (RBC) from blood images. The algorithm picks the suspicious regions for detecting the parasites in the images including the overlapped cells. The RBCs are classified into infected and non-infected cells and find the number of RBCs in each image. Then 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 98 % accuracy for detection and counting of RBCs and 100% for detection and classifying the malaria parasite into one of its four types.

Keywords: Image processing, malaria parasite detection, normalized cross-correlation, malaria diagnosis