Automatic Hepatocyte Quantification from Histological Images: Comparing Pre-smoothing filters

dc.contributor.authorIvanovska, Tetyanaen_US
dc.contributor.authorSchenk, Andreaen_US
dc.contributor.authorDahmen, Utaen_US
dc.contributor.authorHahn, Horst K.en_US
dc.contributor.authorLinsen, Larsen_US
dc.contributor.editorCharl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preimen_US
dc.date.accessioned2014-01-29T17:02:13Z
dc.date.available2014-01-29T17:02:13Z
dc.date.issued2008en_US
dc.description.abstractQuantity 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.en_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biomedicineen_US
dc.identifier.isbn978-3-905674-13-2en_US
dc.identifier.issn2070-5786en_US
dc.identifier.urihttps://doi.org/10.2312/VCBM/VCBM08/143-150en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.4.6 [Image Processing and Computer Vision]: Segmentationen_US
dc.titleAutomatic Hepatocyte Quantification from Histological Images: Comparing Pre-smoothing filtersen_US
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