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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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%. |
format | Online Article Text |
id | pubmed-9049370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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