21 March 2014 Classification of microscopy images of Langerhans islets
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Abstract
Evaluation of images of Langerhans islets is a crucial procedure for planning an islet transplantation, which is a promising diabetes treatment. This paper deals with segmentation of microscopy images of Langerhans islets and evaluation of islet parameters such as area, diameter, or volume (IE). For all the available images, the ground truth and the islet parameters were independently evaluated by four medical experts. We use a pixelwise linear classifier (perceptron algorithm) and SVM (support vector machine) for image segmentation. The volume is estimated based on circle or ellipse fitting to individual islets. The segmentations were compared with the corresponding ground truth. Quantitative islet parameters were also evaluated and compared with parameters given by medical experts. We can conclude that accuracy of the presented fully automatic algorithm is fully comparable with medical experts.
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Jan Švihlík, Jan Kybic, David Habart, Zuzana Berková, Peter Girman, Jan Kříž, Klára Zacharovová, "Classification of microscopy images of Langerhans islets", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341S (21 March 2014); doi: 10.1117/12.2043621; https://doi.org/10.1117/12.2043621
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