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Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy

Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentatio...

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Autores principales: Hu, Hongping, Qiao, Shichang, Hao, Yan, Bai, Yanping, Cheng, Rong, Zhang, Wendong, Zhang, Guojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049370/
https://www.ncbi.nlm.nih.gov/pubmed/35482728
http://dx.doi.org/10.1371/journal.pone.0266973
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author Hu, Hongping
Qiao, Shichang
Hao, Yan
Bai, Yanping
Cheng, Rong
Zhang, Wendong
Zhang, Guojun
author_facet Hu, Hongping
Qiao, Shichang
Hao, Yan
Bai, Yanping
Cheng, Rong
Zhang, Wendong
Zhang, Guojun
author_sort Hu, Hongping
collection PubMed
description Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%.
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spelling pubmed-90493702022-04-29 Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy Hu, Hongping Qiao, Shichang Hao, Yan Bai, Yanping Cheng, Rong Zhang, Wendong Zhang, Guojun PLoS One Research Article Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%. Public Library of Science 2022-04-28 /pmc/articles/PMC9049370/ /pubmed/35482728 http://dx.doi.org/10.1371/journal.pone.0266973 Text en © 2022 Hu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Hu, Hongping
Qiao, Shichang
Hao, Yan
Bai, Yanping
Cheng, Rong
Zhang, Wendong
Zhang, Guojun
Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy
title Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy
title_full Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy
title_fullStr Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy
title_full_unstemmed Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy
title_short Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy
title_sort breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049370/
https://www.ncbi.nlm.nih.gov/pubmed/35482728
http://dx.doi.org/10.1371/journal.pone.0266973
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