The natural history of malaria involves cyclical infection of humans and female Anopheles mosquitoes. In humans, the parasites grow and multiply first in the liver cells and then in the red cells of the blood. The early diagnosis of malaria is required; otherwise, it leads to death. In this study, an effective method for the classification and segmentation of malaria parasite using k-means clustering (KMC) segmentation algorithm and support vector machine (SVM) classifier is presented. Initially, the input blood sample images are given to KMC segmentation technique for segmentation. Then the segmented image is given to statistical features like mean and standard deviation for feature extraction. Then the extracted features are saved in the feature database and used for classification using SVM classifier. The classification of healthy and affected cells of malaria parasite in blood sample images is made by using SVM classifier. Experimental result shows the performance of the proposed system.
