Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

Universality in the merging dynamics of parametric active contours: a study in MRI-based lung segmentation

Measurement of lung ventilation is one of the most reliable techniques of diagnosing pulmonary diseases. The time consuming and bias prone traditional methods using hyperpolarized H${}^{3}$He and ${}^{1}$H magnetic resonance imageries have recently been improved by an automated technique based on multiple active contour evolution. Mapping results from an equivalent thermodynamic model, here we analyse the fundamental dynamics orchestrating the active contour (AC) method. We show that the numerical method is inherently connected to the universal scaling behavior of a classical nucleation-like dynamics. The favorable comparison of the exponent values with the theoretical model render further credentials to our claim.Comment: 4 pages, 4 figure

Regmentation: A New View of Image Segmentation and Registration

Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications

Characterization of structural changes in spinal vertebrae based on perturbations to an adaptive model

Diffuse Idiopathic Skeletal Hyperostosis, or DISH, is a disease characterized by ossification of the entheses and the anterior longitudinal ligament. The diagnosis is made by visual analysis of an X-ray by a professional using the Resnick Criterion. The different experience among professionals and the fact that this criterion is only suitable in advanced stages of the disease make diagnosis difficult. Therefore, this work aims to contribute to the development of an auxiliary diagnostic tool for this disease. For this, a semi-automatic vertebral segmentation algorithm based on active morphological contours was proposed, comparing it with previous work and with segmentations made by experts on two radiographic images. Next, the corners of the vertebrae, where the disease manifests itself, were analyzed in order to characterize images with DISH. To accomplish this, it was assumed symmetry of the vertebrae and a Gaussian distribution of the histograms of those corners to analyze them and calculate two ratios: Left upper corner mean value / Right upper corner mean value (LS/RS) and Left lower corner mean value / Right lower corner mean value (LI/RI), in order to find a differentiating metric between vertebrae with pathology and those without. The results achieved by the algorithm were clearly superior to the previous work and similar to that of the experts. The analysis of pathologic vertebrae revealed a difference in the shift of the distributions of pathologic corners relative to non-pathologic ones, which is not seen in vertebrae without apparent pathology. Regarding the ratios, the LI/RI proved to be particularly effective in differentiating, being closer to 1 when pathology is not present.A Hiperostose Esquelética Idiopática Difusa, ou DISH, é uma doença caracterizada pela ossificação das entéses e do ligamento longitudinal anterior. O diagnóstico é realizado pela análise visual de um raio-X, por um profissional, utilizando o Critério de Resnick. A diferente experiência entre profissionais e o facto de este critério só ser adequado em fases avançadas da doença tornam o diagnóstico difícil. Por isso, este trabalho visa contribuir para o desenvolvimento de um instrumento auxiliar de diagnóstico desta doença. Para isso, foi proposto um algoritmo de segmentação de vertebras, semi-automático, baseado em contornos morfológicos ativos, comparando-o com o trabalho anterior e com as segmentações feitas por especialistas em duas imagens radiográficas. De seguida, foram analisadas as extremidades das vértebras, onde a doença se manifesta, com o objetivo de identificar imagens com DISH. Para tal, assumiu-se a simetria das vértebras e uma distribuição Gaussiana dos histogramas das extremidades para analisar as mesmas e calcular dois rácios: Valor médio do canto superior esquerdo / Valor médio do canto superior direito(LS/RS) e valor médio do canto inferior esquerdo /Valor médio do canto inferior direito(LI/RI), a fim de encontrar uma métrica diferenciadora das vértebras com patologia das não patológicas. Os resultados conseguidos pelo algoritmo foram claramente superiores ao do trabalho anterior e semelhantes ao dos peritos. A análise das vértebras patológicas revelou uma diferença na deslocação das distribuições dos cantos patológicos relativamente aos não patológicos, o que não se verifica em vértebras sem patologia aparente. Relativamente aos rácios, o LI/RI mostrou ser particularmente eficaz na diferenciação, estando mais próximo de 1 quando a patologia não está presente