Design and development of the computer vision algorithm for a real-time breast self-examination

Breast cancer is the most common cancer among women worldwide. Early detection of breast cancer is the key to reduce breast cancer mortality. Breast self-examination (BSE) is considered as the most cost-effective approach available for early breast cancer detection. A large fraction of breast cancer...

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Main Author: Eman, Mohammadi Nejad
Format: text
Language:English
Published: Animo Repository 2014
Online Access:https://animorepository.dlsu.edu.ph/etd_masteral/4604
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_masteral-114422024-04-05T09:25:10Z Design and development of the computer vision algorithm for a real-time breast self-examination Eman, Mohammadi Nejad Breast cancer is the most common cancer among women worldwide. Early detection of breast cancer is the key to reduce breast cancer mortality. Breast self-examination (BSE) is considered as the most cost-effective approach available for early breast cancer detection. A large fraction of breast cancers are actually found by patients using this technique today. In BSE, the patient should use a proper search strategy to cover the whole breast region in order to detect all possible tumors and abnormalities. However, the majority of women don't perform the correct BSE due to the lack of confidence and knowledge on BSE performance. Therefore, there is a need for an application to educate and evaluate the BSE performance. So, women can perform the BSE using a webcam, computer, and the stated application that has the ability to evaluate the BSE performance. The general objective of this thesis is to design and develop the computer vision algorithm to evaluate the BSE performance in terms of covering the entire breast region. In this research, three individual algorithms were developed and implemented. The first algorithm focuses on detecting and tracking the nipples in frames while a woman performs BSE the second algorithm focuses on localizing the breast region and blocks of pixels for making palpation on the breast, and the third algorithm focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. Finally, if all areas in the breast region are palpated, the BSE training is considered as a correct performance in terms of covering and palpating the whole breast region. If any abnormality, such as masses, is detected, then this must be reported to a doctor, who will confirm the presence of this abnormality or mass and proceed to do other confirmatory tests. 2014-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_masteral/4604 Master's Theses English Animo Repository
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
description Breast cancer is the most common cancer among women worldwide. Early detection of breast cancer is the key to reduce breast cancer mortality. Breast self-examination (BSE) is considered as the most cost-effective approach available for early breast cancer detection. A large fraction of breast cancers are actually found by patients using this technique today. In BSE, the patient should use a proper search strategy to cover the whole breast region in order to detect all possible tumors and abnormalities. However, the majority of women don't perform the correct BSE due to the lack of confidence and knowledge on BSE performance. Therefore, there is a need for an application to educate and evaluate the BSE performance. So, women can perform the BSE using a webcam, computer, and the stated application that has the ability to evaluate the BSE performance. The general objective of this thesis is to design and develop the computer vision algorithm to evaluate the BSE performance in terms of covering the entire breast region. In this research, three individual algorithms were developed and implemented. The first algorithm focuses on detecting and tracking the nipples in frames while a woman performs BSE the second algorithm focuses on localizing the breast region and blocks of pixels for making palpation on the breast, and the third algorithm focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. Finally, if all areas in the breast region are palpated, the BSE training is considered as a correct performance in terms of covering and palpating the whole breast region. If any abnormality, such as masses, is detected, then this must be reported to a doctor, who will confirm the presence of this abnormality or mass and proceed to do other confirmatory tests.
format text
author Eman, Mohammadi Nejad
spellingShingle Eman, Mohammadi Nejad
Design and development of the computer vision algorithm for a real-time breast self-examination
author_facet Eman, Mohammadi Nejad
author_sort Eman, Mohammadi Nejad
title Design and development of the computer vision algorithm for a real-time breast self-examination
title_short Design and development of the computer vision algorithm for a real-time breast self-examination
title_full Design and development of the computer vision algorithm for a real-time breast self-examination
title_fullStr Design and development of the computer vision algorithm for a real-time breast self-examination
title_full_unstemmed Design and development of the computer vision algorithm for a real-time breast self-examination
title_sort design and development of the computer vision algorithm for a real-time breast self-examination
publisher Animo Repository
publishDate 2014
url https://animorepository.dlsu.edu.ph/etd_masteral/4604
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