Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised cell nucleus segmentation with active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Fast bilateral filtering for the display of high-dynamic-range images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A fast approximation of the bilateral filter using a signal processing approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Quantity 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.