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Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features

Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are an...

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Autores principales: Bhattacharjee, Subrata, Kim, Cho-Hee, Park, Hyeon-Gyun, Prakash, Deekshitha, Madusanka, Nuwan, Cho, Nam-Hoon, Choi, Heung-Kook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966617/
https://www.ncbi.nlm.nih.gov/pubmed/31817111
http://dx.doi.org/10.3390/cancers11121937
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author Bhattacharjee, Subrata
Kim, Cho-Hee
Park, Hyeon-Gyun
Prakash, Deekshitha
Madusanka, Nuwan
Cho, Nam-Hoon
Choi, Heung-Kook
author_facet Bhattacharjee, Subrata
Kim, Cho-Hee
Park, Hyeon-Gyun
Prakash, Deekshitha
Madusanka, Nuwan
Cho, Nam-Hoon
Choi, Heung-Kook
author_sort Bhattacharjee, Subrata
collection PubMed
description Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas.
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spelling pubmed-69666172020-02-04 Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features Bhattacharjee, Subrata Kim, Cho-Hee Park, Hyeon-Gyun Prakash, Deekshitha Madusanka, Nuwan Cho, Nam-Hoon Choi, Heung-Kook Cancers (Basel) Article Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas. MDPI 2019-12-04 /pmc/articles/PMC6966617/ /pubmed/31817111 http://dx.doi.org/10.3390/cancers11121937 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bhattacharjee, Subrata
Kim, Cho-Hee
Park, Hyeon-Gyun
Prakash, Deekshitha
Madusanka, Nuwan
Cho, Nam-Hoon
Choi, Heung-Kook
Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features
title Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features
title_full Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features
title_fullStr Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features
title_full_unstemmed Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features
title_short Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features
title_sort multi-features classification of prostate carcinoma observed in histological sections: analysis of wavelet-based texture and colour features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966617/
https://www.ncbi.nlm.nih.gov/pubmed/31817111
http://dx.doi.org/10.3390/cancers11121937
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