Detecting and segmenting overlapping red blood cells in microscopic images of thin blood smears

Automated image analysis of slides of thin blood smears can assist with early diagnosis of many diseases. Automated detection and segmentation of red blood cells (RBCs) are prerequisites for any subsequent quantitative highthroughput screening analysis since the manual characterization of the cells is a time-consuming and error-prone task. Overlapping cell regions introduce considerable challenges to detection and segmentation techniques. We propose a novel algorithm that can successfully detect and segment overlapping cells in microscopic images of stained thin blood smears. The algorithm consists 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 that utilizes adaptive meanshift clustering. We employ a…