Stained Blood Cell Detection and Clumped Cell Segmentation Useful for Malaria Parasite Diagnosis

This study presents a new method for splitting clumped blood cells effectively into individual cells and develops a complete framework for automating the detection of malaria parasites in Leishman-stained thin peripheral blood sample images. The images are segmented to extract the foreground information to isolate the RBCs using Chan Vese segmentation algorithm. The noise present in the image is removed using a thresholding method based on the average size of the blood cell. The preprocessed image is subjected to dominant color extraction to detect the stained cells in it. To separate the clumped blood cells, each clumped object is processed by Laplacian of Gaussian edge detection algorithm and then the major axis of the clumped object is computed. The two halves of the segmented object lying above and below the major axis is traversed to find the overlapping points of two clumped cells and the affected cell is separately found out. The robustness and effectiveness of this method has been assessed experimentally with various images collected from Public Health centers.

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