Automated system for malaria parasite identification

Malaria is a serious worldwide health issue which causes globally an expected 3.4 billion individuals in danger of malaria in 2013. Malaria is an entirely preventable and treatable disease [1]. For fast diagnosis and acute treatment of Malaria is important to reduce the death rate. As the parasite changes morphology in its different life stages and its types varies, an experienced technician is required to identify types of malaria parasites in laboratory diagnosis. Current malaria analysis depends essentially on microscopic examination of Giemsa-stained thick and thin blood films, this method is time consuming and routine. According to the World Health Organization, it causes more than 1 million deaths arising from approximately 300 to 500 million infections every year [2]. For the proper medication of patient it is important to identify the species. For acute diagnosis of malaria, numerous analysts have proposed automated malaria detection devices using digital image processing. In this paper, we provide an approach to identify the species of malaria. The system describes image acquisition, preprocessing, segmentation algorithms, and classifier.