Ivanovska, TetyanaSchenk, AndreaDahmen, UtaHahn, Horst K.Linsen, LarsCharl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim2014-01-292014-01-292008978-3-905674-13-22070-5786http://dx.doi.org/10.2312/VCBM/VCBM08/143-150Quantity of hepatocytes in the liver can reveal a lot of information for medical researchers. In our project, it is needed for evaluation of the liver regeneration rate. In this paper, we present a processing pipeline for automatic counting of hepatocytes from images of histological sections. In particular, we propose to introduce a preprocessing step in form of image smoothing. We apply five different smoothing techniques, namely Gaussian smoothing, nonlinear Gaussian smoothing, median filtering, anisotropic diffusion, and minimum description length segmentation, and compare them to each other. The processing pipeline is completed by subsequent automatic thresholding using Otsu s method and hepatocyte detection using Hough transform. We compare the quantification results in terms of quality (sensitivity and specificity rates) against the manually specified ground truth. We discuss the results and limitations of the individual processing steps as well as of the overall automatic quantification approach.Categories and Subject Descriptors (according to ACM CCS): I.4.6 [Image Processing and Computer Vision]: Segmentation