Mammography is used to aid early detection and diagnosis systems. It takes an x-ray image of the breast and can provide a second opinion for radiologists. The earlier detection is made, the better treatment works. Digital mammograms are dealt with by Computer Aided Diagnosis (CAD) systems that can detect and analyze abnormalities in a mammogram. The purpose of this study is to investigate how to categories cropped regions of interest (ROI) from digital mammogram images into two classes; normal and abnormal regions (which contain microcalcifications). The work proposed in this thesis is divided into three stages to provide a concept system for classification between normal and abnormal cases. The first stage is the Segmentation Process, which applies thresholding filters to separate the abnormal objects (foreground) from the breast tissue (background). Moreover, this study has been carried out on mammogram images and mainly on cropped ROI images from different sizes that represent individual microcalcification and ROI that represent a cluster of microcalcifications. The second stage in this thesis is feature extraction. This stage makes use of the segmented ROI images to extract characteristic features that would help in identifying regions of interest. The wavelet transform has been utilized for this process as it provides a variety of features that could be examined in future studies. The third and final stage is classification, where machine learning is applied to be able to distinguish between normal ROI images and ROI images that may contain microcalcifications. The result indicated was that by combining wavelet transform and SVM we can distinguish between regions with normal breast tissue and regions that include microcalcifications.

Mammography is used to aid early detection and diagnosis systems. It takes an x-ray image of the breast and can provide a second opinion for radiologists. The earlier detection is made, the better treatment works. Digital mammograms are dealt with by Computer Aided Diagnosis (CAD) systems that can detect and analyze abnormalities in a mammogram. The purpose of this study is to investigate how to categories cropped regions of interest (ROI) from digital mammogram images into two classes; normal and abnormal regions (which contain microcalcifications). The work proposed in this thesis is divided into three stages to provide a concept system for classification between normal and abnormal cases. The first stage is the Segmentation Process, which applies thresholding filters to separate the abnormal objects (foreground) from the breast tissue (background). Moreover, this study has been carried out on mammogram images and mainly on cropped ROI images from different sizes that represent individual microcalcification and ROI that represent a cluster of microcalcifications. The second stage in this thesis is feature extraction. This stage makes use of the segmented ROI images to extract characteristic features that would help in identifying regions of interest. The wavelet transform has been utilized for this process as it provides a variety of features that could be examined in future studies. The third and final stage is classification, where machine learning is applied to be able to distinguish between normal ROI images and ROI images that may contain microcalcifications. The result indicated was that by combining wavelet transform and SVM we can distinguish between regions with normal breast tissue and regions that include microcalcifications.

O BancoWeb é uma base de imagens para auxilio a pesquisa na área de mamografia digital. Oferece o gerenciamento da base de dados com um conjunto amplo de imagens mamográficas de qualidade apropriada ao processamento digital e, principalmente, comparação entre diferentes esquemas. O sistema de busca da base de dados/imagens mamográficas está disponível desde 2010, sendo on-line e gratuito. Com os avanços em pesquisas durante todos estes anos, a necessidade e demanda de melhorias, mudanças e incrementos no sistema de gerenciamento e buscas foram surgindo. Por isso, os objetivos deste trabalho estão em desenvolver e aperfeiçoar recursos na busca de imagens, tipos de achado, permitir a classificação pelo sistema CAD, além de implementar um novo sistema de gerenciamento e armazenamento de imagens de um novo phantom, desenvolvido como resultado de pesquisa ainda mais recente no grupo. Testes comparativos com outras bases análogas que disponibilizam imagens mamográficas para download faz do BancoWeb uma ferramenta única e poderosa para o pesquisador e interessado da área, oferecendo mais opções que as demais bases disponíveis atualmente. / BancoWeb is a database of images to aid research in the field of digital mammography. It offers the management of the database with a wide set of mammographic images of appropriate quality to the digital processing and, mainly, comparison between different schemes. The database search system/mammographic images are available since 2010, being online and free. With advances in research over the years, the need for and demand for improvements, changes, and increases in the management and search system have been emerging. Therefore, the objectives of this work are in development and improvement of resources in the search of images, types of find, allow the selection of CAD, in addition to implementing a new system of management and storage of images of a new phantom, developed as a result of even more recent research in the group. Comparative tests with other analogous databases that provide mammographic images for download makes BancoWeb a unique and powerful tool for the researcher and the interested party, offering more options than the other databases currently available.

O BancoWeb é uma base de imagens para auxilio a pesquisa na área de mamografia digital. Oferece o gerenciamento da base de dados com um conjunto amplo de imagens mamográficas de qualidade apropriada ao processamento digital e, principalmente, comparação entre diferentes esquemas. O sistema de busca da base de dados/imagens mamográficas está disponível desde 2010, sendo on-line e gratuito. Com os avanços em pesquisas durante todos estes anos, a necessidade e demanda de melhorias, mudanças e incrementos no sistema de gerenciamento e buscas foram surgindo. Por isso, os objetivos deste trabalho estão em desenvolver e aperfeiçoar recursos na busca de imagens, tipos de achado, permitir a classificação pelo sistema CAD, além de implementar um novo sistema de gerenciamento e armazenamento de imagens de um novo phantom, desenvolvido como resultado de pesquisa ainda mais recente no grupo. Testes comparativos com outras bases análogas que disponibilizam imagens mamográficas para download faz do BancoWeb uma ferramenta única e poderosa para o pesquisador e interessado da área, oferecendo mais opções que as demais bases disponíveis atualmente. / BancoWeb is a database of images to aid research in the field of digital mammography. It offers the management of the database with a wide set of mammographic images of appropriate quality to the digital processing and, mainly, comparison between different schemes. The database search system/mammographic images are available since 2010, being online and free. With advances in research over the years, the need for and demand for improvements, changes, and increases in the management and search system have been emerging. Therefore, the objectives of this work are in development and improvement of resources in the search of images, types of find, allow the selection of CAD, in addition to implementing a new system of management and storage of images of a new phantom, developed as a result of even more recent research in the group. Comparative tests with other analogous databases that provide mammographic images for download makes BancoWeb a unique and powerful tool for the researcher and the interested party, offering more options than the other databases currently available.

Made available in DSpace on 2016-08-16T18:18:41Z (GMT). No. of bitstreams: 1 Tese Daniel Duarte Costa.pdf: 3067192 bytes, checksum: b9a8d78583596a2e1dff6298c4a89014 (MD5) Previous issue date: 2012-12-06 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Breast cancer is the leading cause of cancer death among women in Western countries. To improve the accuracy of diagnosis by radiologists and doing it so early, new computer vision systems have been developed and improved with the passage of time. Some methods of the detection and classification of lesions in mammography images for computer systems diagnostic (CAD) were developed using different statistical techniques. In this thesis, we present methodologies of CADs systems to detect and classify mass regions in mammographic images, from two image databases: DDSM and MIAS. The results show that it is possible by these methods to obtain a detection rate of up to 96% of mass regions, using efficient coding technique and K-means clustering algorithm. To classify regions in mass or non-mass correctly, was obtained a success rate up to 90% using the independent component analysis (ICA) and linear discriminant analysis (LDA). From these results generated a web application, called SADIM (Sistema de Auxílio a Diagnóstico de Imagem Mamográfica), which can be used by any registered professional. / O câncer de mama é a principal causa de morte por câncer na população feminina dos países ocidentais. Para melhorar a precisão do diagnóstico por radiologistas e fazê-lo de forma precoce, novos sistemas de visão computacional têm sido criados e melhorados com o decorrer do tempo. Alguns métodos de detecção e classificação da lesão em imagens radiológicas, por sistemas de diagnósticos por computador (CAD), foram desenvolvidos utilizando diferentes técnicas estatísticas. Neste trabalho, apresentam-se metodologias de sistemas CADs para detectar e classificar regiões de massa em imagens mamográficas, oriundas de duas bases de imagens: DDSM e MIAS. Os resultados mostram que é possível, através destas metodologias, obter uma taxa de detecção de até 96% das regiões de massa, utilizando a técnica de codificação eficiente com o algoritmo de agrupamento k-means, e classificar corretamente as regiões de massa em até 90% utilizando-se das técnicas de análise de componentes independentes (ICA) e análise discriminante linear (LDA). A partir destes resultados gerou-se uma aplicação web, denominada SADIM (Sistema de Auxílio a Diagnóstico de Imagem Mamográfica), que pode ser utilizado por qualquer profissional cadastrado. Palavras-chave: processamento de imagens médicas; diagnóstico auxiliado por computador; mamografias análise de imagens; codificação eficiente.

Dans le cadre de cette thèse, nous nous sommes intéressés à l’étude de l’apport d’une aide assistée par ordinateur au diagnostic de certaines maladies dégénératives du cerveau, en explorant les images de tomographie par émission de positons, par des techniques de traitement d’image et d’analyse statistique.Nous nous sommes intéressés à la représentation corticale des 116 régions anatomiques, en associant à chacune d’elles un vecteur d’attribut issu du calcul des 4 premiers moments des intensités de voxels, et en y incluant par ailleurs l’entropie. Sur la base de l’aire de courbes ROC, nous avons établi qualitativement la pertinence de chacune des régions anatomiques, en fonction du nombre de paramètres du vecteur d’attribut qui lui était associé, pour séparer le groupe des sujets sains de celui des sujets atteints de la maladie d’Alzheimer. Dans notre étude nous avons proposé une nouvelle approche de sélection de régions les plus pertinentes, nommée "combination matrix", en se basant sur un système combinatoire. Chaque région est caractérisée par les différentes combinaisons de son vecteur d’attribut. L’introduction des régions les plus pertinentes(en terme de pouvoir de séparation des sujets) dans le classificateur supervisé SVM nous a permis d’obtenir, malgré la réduction de dimension opérée, un taux de classification meilleur que celui obtenu en utilisant l’ensemble des régions. / Our research focuses on presenting a novel computer-aided diagnosis technique for brain Positrons Emission Tomography (PET) images. It processes and analyzes quantitatively these images, in order to better characterize and extract meaningful information for medical diagnosis. Our contribution is to present a new method of classifying brain 18 FDG PET images. Brain images are first segmented into 116 Regions Of Interest (ROI) using an atlas. After computing some statistical features (mean, standarddeviation, skewness, kurtosis and entropy) on these regions’ histogram, we defined a Separation Power Factor (SPF) associated to each region. This factor quantifies the ability of each region to separate neurodegenerative diseases like Alzheimer disease from Healthy Control (HC) brain images. A novel region-based approach is developed to classify brain 18FDG-PET images. The motivation of this work is to identify the best regional features for separating HC from AD patients, in order to reduce the number of features required to achieve an acceptable classification result while reducing computational time required for the classification task.