Counting clustered cells using distance mapping

Cell segmentation in microscopic images is inherently challenging due to the embedded optical artifacts and the overlapping of cells. Proper segmentation can help for shape analysis, motion tracking and cell counting. We present a framework for cell segmentation and counting by detection of cell centroids in microscopic images. The method is specifically designed for counting circular cells with a high probability of occlusion. The proposed algorithm has been implemented and evaluated on images of fluorescent cell population, collected from the Broad Bioimage Benchmark Collection (www.broad.mit.edu/bbbc), with different degrees of overlap probability. The experimental results show an excellent accuracy of 92% for cell counting even at a very high 60% overlap probability.