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Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas

Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasi...

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Autores principales: Hwang, Hae-Gil, Choi, Hyun-Ju, Lee, Byeong-Il, Yoon, Hye-Kyoung, Nam, Sang-Hee, Choi, Heung-Kook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4615965/
https://www.ncbi.nlm.nih.gov/pubmed/16308473
http://dx.doi.org/10.1155/2005/526083
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author Hwang, Hae-Gil
Choi, Hyun-Ju
Lee, Byeong-Il
Yoon, Hye-Kyoung
Nam, Sang-Hee
Choi, Heung-Kook
author_facet Hwang, Hae-Gil
Choi, Hyun-Ju
Lee, Byeong-Il
Yoon, Hye-Kyoung
Nam, Sang-Hee
Choi, Heung-Kook
author_sort Hwang, Hae-Gil
collection PubMed
description Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.
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spelling pubmed-46159652016-01-12 Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas Hwang, Hae-Gil Choi, Hyun-Ju Lee, Byeong-Il Yoon, Hye-Kyoung Nam, Sang-Hee Choi, Heung-Kook Cell Oncol Other Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function. IOS Press 2005 2005-11-22 /pmc/articles/PMC4615965/ /pubmed/16308473 http://dx.doi.org/10.1155/2005/526083 Text en Copyright © 2005 Hindawi Publishing Corporation and the authors.
spellingShingle Other
Hwang, Hae-Gil
Choi, Hyun-Ju
Lee, Byeong-Il
Yoon, Hye-Kyoung
Nam, Sang-Hee
Choi, Heung-Kook
Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas
title Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas
title_full Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas
title_fullStr Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas
title_full_unstemmed Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas
title_short Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas
title_sort multi-resolution wavelet-transformed image analysis of histological sections of breast carcinomas
topic Other
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4615965/
https://www.ncbi.nlm.nih.gov/pubmed/16308473
http://dx.doi.org/10.1155/2005/526083
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