Thesis (Ph. D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 123 pages. Includes vita. Includes bibliographical references.

Segmenting brain MR scans could be highly benecial for diagnosing, treating and evaluating the progress of specic diseases. Up to this point, manual segmentation,performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. Many non-automatic and semi automatic methods have been proposed in the literature in order to segment MR brain images, but the levelof accuracy is not comparable with manual segmentation. The aim of this project is to implement and make a preliminary evaluation of a method based on machine learning technique for segmenting gray matter (GM),white matter (WM) and cerebrospinal uid (CSF) of brain MR scans using images available within the open MICCAI grand challenge (MRBrainS13).The proposed method employs supervised articial neural network based autocontext algorithm, exploiting intensity-based, spatial-based and shape model-basedlevel set segmentation results as features of the network. The obtained average results based on Dice similarity index were 97.73%, 95.37%, 82.76%, 88.47% and 84.78% for intracranial volume, brain (WM + GM), CSF, WM and GM respectively. This method achieved competitive results with considerably shorter required training time in MRBrainsS13 challenge.

Este trabalho apresenta novos métodos para análise de formas tridimensionais dentro do contexto de visão computacional, destacando-se o uso das transformadas wavelets 1D, 2D e 3D, as quais proporcionam uma análise multi-escala das formas estudadas. As formas analisadas se dividem em três tipos diferentes, dependendo da sua representação matemática: f(t)=(x(t),y(t),z(t)), f(x,y)=z e f(x,y,z)=w. Cada tipo de forma é analisado por um método melhor adaptado. Primeiramente, tais formas passam por uma rotina de pré-processamento e, em seguida, pela caracterização por meio da aplicação das transformadas wavelet 1D, 2D e 3D para as respectivas formas. Esta aplicação nos permite extrair características que sejam invariantes à rotação e translação, levando em consideração alguns conceitos matemáticos da geometria diferencial. Destaca-se também neste trabalho a não obrigatoriedade de parametrização das formas. Os resultados obtidos a partir de formas extraídas de imagens médicas e dados biológicos, que justificam este trabalho, são apresentados. / This work presents new methods for three-dimensional shape analysis in the context of computational vision, being emphasized the use of 1D, 2D and 3D wavelet transforms, which provide a multiscale analysis of the studied shapes. The analyzed shapes are divided in three different types depending on their representation: f(t)=(x(t),y(t),z(t)), f(x,y)=z and f(x,y,z)=w. Each type of shape is analyzed by a more suitable method. Firstly, such shapes undergo a pre-processing procedure followed by the characterization using the 1D, 2D or 3D wavelet transform, depending on its representation. This application allows to extract features that are rotation- and translation-invariant, based on some mathematical concepts of differential geometry. In this work, we emphasize that it is not necessary to use the parameterized version of the 2D and 3D shapes. The experimental results obtained from shapes extracted from medical and biological images, that corroborate the introduced methods, are presented.

Este trabalho apresenta novos métodos para análise de formas tridimensionais dentro do contexto de visão computacional, destacando-se o uso das transformadas wavelets 1D, 2D e 3D, as quais proporcionam uma análise multi-escala das formas estudadas. As formas analisadas se dividem em três tipos diferentes, dependendo da sua representação matemática: f(t)=(x(t),y(t),z(t)), f(x,y)=z e f(x,y,z)=w. Cada tipo de forma é analisado por um método melhor adaptado. Primeiramente, tais formas passam por uma rotina de pré-processamento e, em seguida, pela caracterização por meio da aplicação das transformadas wavelet 1D, 2D e 3D para as respectivas formas. Esta aplicação nos permite extrair características que sejam invariantes à rotação e translação, levando em consideração alguns conceitos matemáticos da geometria diferencial. Destaca-se também neste trabalho a não obrigatoriedade de parametrização das formas. Os resultados obtidos a partir de formas extraídas de imagens médicas e dados biológicos, que justificam este trabalho, são apresentados. / This work presents new methods for three-dimensional shape analysis in the context of computational vision, being emphasized the use of 1D, 2D and 3D wavelet transforms, which provide a multiscale analysis of the studied shapes. The analyzed shapes are divided in three different types depending on their representation: f(t)=(x(t),y(t),z(t)), f(x,y)=z and f(x,y,z)=w. Each type of shape is analyzed by a more suitable method. Firstly, such shapes undergo a pre-processing procedure followed by the characterization using the 1D, 2D or 3D wavelet transform, depending on its representation. This application allows to extract features that are rotation- and translation-invariant, based on some mathematical concepts of differential geometry. In this work, we emphasize that it is not necessary to use the parameterized version of the 2D and 3D shapes. The experimental results obtained from shapes extracted from medical and biological images, that corroborate the introduced methods, are presented.

Fúze obrazu je dnes jednou z nejběžnějších avšak stále velmi diskutovanou oblastí v lékařském zobrazování a hraje důležitou roli ve všech oblastech lékařské péče jako je diagnóza, léčba a chirurgie. V této dizertační práci jsou představeny tři projekty, které jsou velmi úzce spojeny s oblastí fúze medicínských dat. První projekt pojednává o 3D CT subtrakční angiografii dolních končetin. V práci je využito kombinace kontrastních a nekontrastních dat pro získání kompletního cévního stromu. Druhý projekt se zabývá fúzí DTI a T1 váhovaných MRI dat mozku. Cílem tohoto projektu je zkombinovat stukturální a funkční informace, které umožňují zlepšit znalosti konektivity v mozkové tkáni. Třetí projekt se zabývá metastázemi v CT časových datech páteře. Tento projekt je zaměřen na studium vývoje metastáz uvnitř obratlů ve fúzované časové řadě snímků. Tato dizertační práce představuje novou metodologii pro klasifikaci těchto metastáz. Všechny projekty zmíněné v této dizertační práci byly řešeny v rámci pracovní skupiny zabývající se analýzou lékařských dat, kterou vedl pan Prof. Jiří Jan. Tato dizertační práce obsahuje registrační část prvního a klasifikační část třetího projektu. Druhý projekt je představen kompletně. Další část prvního a třetího projektu, obsahující specifické předzpracování dat, jsou obsaženy v disertační práci mého kolegy Ing. Romana Petera.