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Automatic malaria diagnosis requires the segmentation of the erythrocytes from the microscopic blood smears images. In this paper, the system for the analysis of the erythrocyte to detect the malaria infection has been proposed. The challenging issues are: preprocessing, foreground extraction, clump erythrocyte segmentation and infected erythrocyte detection. The non-uniform illumination and other distortions of the acquired microscopic images are corrected by using median filtering technique. The foreground regions,  i.e.  erythrocyte regions are segmented from the other blood components in the smears by using Otsu’s thresholding along with morphological filtering. The clump erythrocytes are segmented based on the inherent geometrical property of the concavity point detection of the clump which is independent of any geometrical deformation or image acquisition errors. Further, the segmented erythrocytes are analysed to detect the infected erythrocytes using thresholding technique based on the “pseudo-valley” concept. The experimental analyses of the proposed method are carried out on three different sets of database. An accuracy of 97.95% was observed for the erythrocyte segmentation, with an improvement in accuracy of 8.76% and 0.87% as compared to classical watershed and marker controlled watershed segmentation technique respectively. Moreover, the infected erythrocyte was detected with an accuracy of 88.57%.

Automatic Detection of Malaria Infected Erythrocytes Based on the Concavity Point Identification and Pseudo-Valley Based Thresholding