Detection and segmentation of overlapping red blood cells in microscopic medical images of stained peripheral blood smears

Automated image analysis of slides of stained peripheral blood smears assists with early diagnosis of blood disorders. Automated detection and segmentation of the cells is a prerequisite for any subsequent quantitative analysis. Overlapping cell regions introduce considerable challenges to detection and segmentation techniques. Throughout this thesis, we propose a novel algorithm that can successfully detect and segment overlapping cells in microscopic images of stained peripheral blood smears. The algorithm is composed of three steps. In the first step, the input image is binarized to obtain the binary mask of the image. The second step accomplishes a reliable cell center localization approach that employs adaptive mean-shift clustering. The third step fulfills the cell segmentation purpose by obtaining the boundary of each cell utilizing a Gradient Vector Flow (GVF) driven snake algorithm. We compare the experimental results of our methodology with those reported in the most current literature. Additionally, performance of the proposed method is evaluated by comparing both cell detection and cell segmentation results with those produced manually. The method is systematically tested on 100 image patches comprising overlapping cell regions and containing more than 3800 cells. We evaluate the performance of the proposed cell detection step using precision/ TP/FP and FN rates. Moreover, the cell segmentation step is assessed employing sensitivity, specificity and Jaccard index.