A Novel Algorithm for Segmentation of Parasites in Thin Blood Smears From Microscopy Using Type II Fuzzy Sets and Inverse Gaussian Gradient

Malaria is the leading health problem globally in the equatorial regions. Even after campaigning and control by the World Health Organization (WHO), malaria is a high mortality-causing infectious disease due to inappropriate and late diagnosis. To prevent getting affected by malaria, the diagnosis should be in the early stage and accurate. This research presents an innovative approach for erythrocyte segmentation and malaria parasite detection from microscopic images. In addition, the author utilizes Einstein t-conorm function to generate new fuzzy membership function which is used to segment the infected regions of the blood cells in microscopic images. Finally, the inverse Gaussian gradient function is used for the final segmentation. This technique is compared with state-of-the-art segmentation techniques such as k-means, fuzzy c-means segmentation, and spatial intuitionistic fuzzy c-means (SIFCM) segmentation approaches. Experimental results are validated qualitatively, and it is deduced that the proposed method is the robust segmentation method for malaria parasite detection.