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Title of Journal: SIViP

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Abbravation: Signal, Image and Video Processing

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Springer London

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1863-1711

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Automated detection and classification of nuclei i

Authors: Kosmas Dimitropoulos Panagiotis Barmpoutis Triantafyllia Koletsa Ioannis Kostopoulos Nikos Grammalidis
Publish Date: 2016/06/14
Volume: 11, Issue: 1, Pages: 145-153
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Abstract

In this paper we propose a novel framework for the detection and classification of centroblasts CB in follicular lymphoma FL tissue samples stained with PAX5 and HE stains and sliced at 1 upmu m thickness level By employing PAX5 immunohistochemistry we facilitate the segmentation of nuclei while the use of HE stain enables us to extract textural information related to histological characteristics used by pathologists in the diagnosis of FL grading For the segmentation of nuclei in PAX5stained images we initially apply an energy minimization technique based on graph cuts and then we propose a novel algorithm for the separation of overlapped nuclei inspired by the clustering of largescale visual vocabularies The morphological characteristics of nuclei extracted from PAX5stained images are combined with a number of textural characteristics identified in HE images through a Bayesian network classifier which aims to model pathologists’ knowledge used in FL grading Experimental results have already shown the great potential of the proposed methodology providing an average Fscore of 9456

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