Segmentation of malaria parasite candidates from thick blood smear microphotographs image using active contour without edge

Abstract

Malaria is a serious health problem in Indonesia caused by malaria parasites. Early detection of Malaria is an important step to an effective treatment. Malaria parasite identification should be carried out based on observation on at least 100 fields view strong magnification of thick blood smears. Malaria parasite detection process is usually carried out with a microscope observation. But it consumes too much time and the number experts are limited. To overcome these obstacles, we developed a computer aided diagnosis system to automatically detecting malaria parasites. Parasite image segmentation is an important step in the detection process. But segmentation of malaria parasite that consists of a nucleus and cytoplasm in a thick blood smear is not easy because the boundary between object and background is not clear and has a low contrast. This study proposed a solution to the problem of segmentation of malaria candidate parasite candidates from thick blood smears. The proposed method focused on image enhancement and segmentation steps. Image enhancement consists of lowpass filtering to reduce noise and contrast stretching to increase contrast. Segmentation is used to detect object using active contour without edge, then erosion, dilation, masking, contrast stretching, and thresholding. The result showed that the proposed method is capable to segment malaria parasite candidates from thick blood smear with 97.57% accuracy, 12.04% (283 pixels) false negative rate (FNR), and 6.87% (202 pixels) false discovery rate (FDR), from 19600 pixels total in each image.