An intelligent diagnostic system for malaria - Aimi Salihah Abdul Nasir (School of Mechatronic Engineering, Universiti Malaysia Perlis, 2015)
This research has developed an intelligent diagnostic system for malaria which consists of image processing and intelligent classifier for the purpose of malaria diagnosis. A 3-stage classification of intelligent diagnostic system can be used as an early detection for malaria based on the classification of blood samples between normal and malaria on the first stage, and further classify the malaria sample as either P. falciparum or P. vivax species on the second stage, along with its four different life-cycle stages which are young trophozoite, mature trophozoite, schizont and gametocyte on the final stage. In order to perform the diagnosis process, the blood images were processed with various image processing techniques such as contrast enhancement and image segmentation for obtaining a fully segmented malaria parasite. As for contrast enhancement, this study proposed modified global and modified linear contrast stretching based on total pixel approach, as well as modified global and modified linear contrast stretching based on pixel level approach. After the image has been enhanced, the malaria image was segmented using different types of clustering algorithms.