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Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification
Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern cl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3773385/ https://www.ncbi.nlm.nih.gov/pubmed/24069067 http://dx.doi.org/10.1155/2013/829461 |
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author | Loukas, C. Kostopoulos, S. Tanoglidi, A. Glotsos, D. Sfikas, C. Cavouras, D. |
author_facet | Loukas, C. Kostopoulos, S. Tanoglidi, A. Glotsos, D. Sfikas, C. Cavouras, D. |
author_sort | Loukas, C. |
collection | PubMed |
description | Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I–III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination. |
format | Online Article Text |
id | pubmed-3773385 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-37733852013-09-25 Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification Loukas, C. Kostopoulos, S. Tanoglidi, A. Glotsos, D. Sfikas, C. Cavouras, D. Comput Math Methods Med Research Article Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I–III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination. Hindawi Publishing Corporation 2013 2013-08-31 /pmc/articles/PMC3773385/ /pubmed/24069067 http://dx.doi.org/10.1155/2013/829461 Text en Copyright © 2013 C. Loukas et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Loukas, C. Kostopoulos, S. Tanoglidi, A. Glotsos, D. Sfikas, C. Cavouras, D. Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification |
title | Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification |
title_full | Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification |
title_fullStr | Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification |
title_full_unstemmed | Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification |
title_short | Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification |
title_sort | breast cancer characterization based on image classification of tissue sections visualized under low magnification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3773385/ https://www.ncbi.nlm.nih.gov/pubmed/24069067 http://dx.doi.org/10.1155/2013/829461 |
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