Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética

Detalhes bibliográficos
Autor(a) principal: Rafaela Inês Pires Pinto
Data de Publicação: 2017
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/105286
Resumo: Multiple sclerosis is the most commonly diagnosed neurological disorder in young adults with unexplained causes and major repercussions in the lives of patients, urging researchers to actively search for answers. Although the disease cannot be cured or prevented, the available treatments nowadays reduce its severity and delay its progression. It is becoming increasingly necessary to use imaging techniques and also image processing and analysis techniques, to help doctors perform an early diagnosis and start appropriate treatment in order to provide a better quality of life for the patient. Several approaches based on automatic segmentation of multiple sclerosis lesions have been extensively investigated in recent years for this purpose. This project was developed, firstly, with the recognition of the steps necessary to implement and optimize an image processing and analysis methodology for automatic segmentation of MS lesions, and secondly, by the exploration of pre-processing, segmentation and classification techniques for objective and quantitative characterization of the lesions. This work will also be discussed basic concepts of multiple sclerosis disease and magnetic resonance imaging (MRI), as well as the bibliographical study of some of the currently existing methodologies. The methodology developed in this dissertation was based on the implementation of several pre-processing algorithms for noise smoothing and removal, non-cerebral tissue removal, contrast correction and normalization of images intensity. For lesion segmentation was applied to the study of neural networks, a very promising and current approach to the proposed problem, and to classify were extracted and analyzed some characteristics of the lesions through shape and size. It is intended that this new methodology is flexible and allow the testing and analysis of the results. The results obtained demonstrate that pre-processing techniques are essential to the subsequent steps allowing better image quality. Segmentation of lesions through the use of neural networks proved to be appropriate for this study as shown by the metrics analyzed, with a structural similarity index very close to 1, mean absolute error rate of 3.8% and Dice coefficient of 0.58. Finally, the various practical applications performed was possible to demonstrate the usefulness and adequacy of image processing and analysis techniques in the study and detection of multiple sclerosis lesions through MR images.
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spelling Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância MagnéticaCiências médicas e da saúdeMedical and Health sciencesMultiple sclerosis is the most commonly diagnosed neurological disorder in young adults with unexplained causes and major repercussions in the lives of patients, urging researchers to actively search for answers. Although the disease cannot be cured or prevented, the available treatments nowadays reduce its severity and delay its progression. It is becoming increasingly necessary to use imaging techniques and also image processing and analysis techniques, to help doctors perform an early diagnosis and start appropriate treatment in order to provide a better quality of life for the patient. Several approaches based on automatic segmentation of multiple sclerosis lesions have been extensively investigated in recent years for this purpose. This project was developed, firstly, with the recognition of the steps necessary to implement and optimize an image processing and analysis methodology for automatic segmentation of MS lesions, and secondly, by the exploration of pre-processing, segmentation and classification techniques for objective and quantitative characterization of the lesions. This work will also be discussed basic concepts of multiple sclerosis disease and magnetic resonance imaging (MRI), as well as the bibliographical study of some of the currently existing methodologies. The methodology developed in this dissertation was based on the implementation of several pre-processing algorithms for noise smoothing and removal, non-cerebral tissue removal, contrast correction and normalization of images intensity. For lesion segmentation was applied to the study of neural networks, a very promising and current approach to the proposed problem, and to classify were extracted and analyzed some characteristics of the lesions through shape and size. It is intended that this new methodology is flexible and allow the testing and analysis of the results. The results obtained demonstrate that pre-processing techniques are essential to the subsequent steps allowing better image quality. Segmentation of lesions through the use of neural networks proved to be appropriate for this study as shown by the metrics analyzed, with a structural similarity index very close to 1, mean absolute error rate of 3.8% and Dice coefficient of 0.58. Finally, the various practical applications performed was possible to demonstrate the usefulness and adequacy of image processing and analysis techniques in the study and detection of multiple sclerosis lesions through MR images.2017-06-272017-06-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/105286TID:201802490porRafaela Inês Pires Pintoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T13:06:04Zoai:repositorio-aberto.up.pt:10216/105286Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:33:30.413895Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética
title Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética
spellingShingle Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética
Rafaela Inês Pires Pinto
Ciências médicas e da saúde
Medical and Health sciences
title_short Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética
title_full Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética
title_fullStr Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética
title_full_unstemmed Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética
title_sort Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética
author Rafaela Inês Pires Pinto
author_facet Rafaela Inês Pires Pinto
author_role author
dc.contributor.author.fl_str_mv Rafaela Inês Pires Pinto
dc.subject.por.fl_str_mv Ciências médicas e da saúde
Medical and Health sciences
topic Ciências médicas e da saúde
Medical and Health sciences
description Multiple sclerosis is the most commonly diagnosed neurological disorder in young adults with unexplained causes and major repercussions in the lives of patients, urging researchers to actively search for answers. Although the disease cannot be cured or prevented, the available treatments nowadays reduce its severity and delay its progression. It is becoming increasingly necessary to use imaging techniques and also image processing and analysis techniques, to help doctors perform an early diagnosis and start appropriate treatment in order to provide a better quality of life for the patient. Several approaches based on automatic segmentation of multiple sclerosis lesions have been extensively investigated in recent years for this purpose. This project was developed, firstly, with the recognition of the steps necessary to implement and optimize an image processing and analysis methodology for automatic segmentation of MS lesions, and secondly, by the exploration of pre-processing, segmentation and classification techniques for objective and quantitative characterization of the lesions. This work will also be discussed basic concepts of multiple sclerosis disease and magnetic resonance imaging (MRI), as well as the bibliographical study of some of the currently existing methodologies. The methodology developed in this dissertation was based on the implementation of several pre-processing algorithms for noise smoothing and removal, non-cerebral tissue removal, contrast correction and normalization of images intensity. For lesion segmentation was applied to the study of neural networks, a very promising and current approach to the proposed problem, and to classify were extracted and analyzed some characteristics of the lesions through shape and size. It is intended that this new methodology is flexible and allow the testing and analysis of the results. The results obtained demonstrate that pre-processing techniques are essential to the subsequent steps allowing better image quality. Segmentation of lesions through the use of neural networks proved to be appropriate for this study as shown by the metrics analyzed, with a structural similarity index very close to 1, mean absolute error rate of 3.8% and Dice coefficient of 0.58. Finally, the various practical applications performed was possible to demonstrate the usefulness and adequacy of image processing and analysis techniques in the study and detection of multiple sclerosis lesions through MR images.
publishDate 2017
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