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Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer

Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coh...

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Autores principales: Almalki, Yassir Edrees, Soomro, Toufique Ahmed, Irfan, Muhammad, Alduraibi, Sharifa Khalid, Ali, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915058/
https://www.ncbi.nlm.nih.gov/pubmed/35271015
http://dx.doi.org/10.3390/s22051868
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author Almalki, Yassir Edrees
Soomro, Toufique Ahmed
Irfan, Muhammad
Alduraibi, Sharifa Khalid
Ali, Ahmed
author_facet Almalki, Yassir Edrees
Soomro, Toufique Ahmed
Irfan, Muhammad
Alduraibi, Sharifa Khalid
Ali, Ahmed
author_sort Almalki, Yassir Edrees
collection PubMed
description Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method.
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spelling pubmed-89150582022-03-12 Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer Almalki, Yassir Edrees Soomro, Toufique Ahmed Irfan, Muhammad Alduraibi, Sharifa Khalid Ali, Ahmed Sensors (Basel) Article Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method. MDPI 2022-02-26 /pmc/articles/PMC8915058/ /pubmed/35271015 http://dx.doi.org/10.3390/s22051868 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Almalki, Yassir Edrees
Soomro, Toufique Ahmed
Irfan, Muhammad
Alduraibi, Sharifa Khalid
Ali, Ahmed
Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer
title Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer
title_full Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer
title_fullStr Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer
title_full_unstemmed Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer
title_short Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer
title_sort impact of image enhancement module for analysis of mammogram images for diagnostics of breast cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915058/
https://www.ncbi.nlm.nih.gov/pubmed/35271015
http://dx.doi.org/10.3390/s22051868
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