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Automated Segmentation of Nuclei in Breast Cancer Histopathology Images

The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei peripher...

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Autores principales: Paramanandam, Maqlin, O’Byrne, Michael, Ghosh, Bidisha, Mammen, Joy John, Manipadam, Marie Therese, Thamburaj, Robinson, Pakrashi, Vikram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5029866/
https://www.ncbi.nlm.nih.gov/pubmed/27649496
http://dx.doi.org/10.1371/journal.pone.0162053
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author Paramanandam, Maqlin
O’Byrne, Michael
Ghosh, Bidisha
Mammen, Joy John
Manipadam, Marie Therese
Thamburaj, Robinson
Pakrashi, Vikram
author_facet Paramanandam, Maqlin
O’Byrne, Michael
Ghosh, Bidisha
Mammen, Joy John
Manipadam, Marie Therese
Thamburaj, Robinson
Pakrashi, Vikram
author_sort Paramanandam, Maqlin
collection PubMed
description The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods—Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets.
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spelling pubmed-50298662016-10-10 Automated Segmentation of Nuclei in Breast Cancer Histopathology Images Paramanandam, Maqlin O’Byrne, Michael Ghosh, Bidisha Mammen, Joy John Manipadam, Marie Therese Thamburaj, Robinson Pakrashi, Vikram PLoS One Research Article The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods—Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets. Public Library of Science 2016-09-20 /pmc/articles/PMC5029866/ /pubmed/27649496 http://dx.doi.org/10.1371/journal.pone.0162053 Text en © 2016 Paramanandam et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Paramanandam, Maqlin
O’Byrne, Michael
Ghosh, Bidisha
Mammen, Joy John
Manipadam, Marie Therese
Thamburaj, Robinson
Pakrashi, Vikram
Automated Segmentation of Nuclei in Breast Cancer Histopathology Images
title Automated Segmentation of Nuclei in Breast Cancer Histopathology Images
title_full Automated Segmentation of Nuclei in Breast Cancer Histopathology Images
title_fullStr Automated Segmentation of Nuclei in Breast Cancer Histopathology Images
title_full_unstemmed Automated Segmentation of Nuclei in Breast Cancer Histopathology Images
title_short Automated Segmentation of Nuclei in Breast Cancer Histopathology Images
title_sort automated segmentation of nuclei in breast cancer histopathology images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5029866/
https://www.ncbi.nlm.nih.gov/pubmed/27649496
http://dx.doi.org/10.1371/journal.pone.0162053
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