Multi-scale active shape description in medical imaging

Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed

Multiscale Active Contours

We propose a new multiscale image segmentation model, based on the active contour/snake model and the Polyakov action. The concept of scale, general issue in physics and signal processing, is introduced in the active contour model, which is a well-known image segmentation model that consists of evolving a contour in images toward the boundaries of objects. The Polyakov action, introduced in image processing by Sochen-Kimmel-Malladi in Sochen et al. (1998), provides an efficient mathematical framework to define a multiscale segmentation model because it generalizes the concept of harmonic maps embedded in higher-dimensional Riemannian manifolds such as multiscale images. Our multiscale segmentation model, unlike classical multiscale segmentations which work scale by scale to speed up the segmentation process, uses all scales simultaneously, i.e. the whole scale space, to introduce the geometry of multiscale images in the segmentation process. The extracted multiscale structures will be useful to efficiently improve the robustness and the performance of standard shape analysis techniques such as shape recognition and shape registration. Another advantage of our method is to use not only the Gaussian scale space but also many other multiscale spaces such as the Perona-Malik scale space, the curvature scale space or the Beltrami scale space. Finally, this multiscale segmentation technique is coupled with a multiscale edge detecting function based on the gradient vector flow model, which is able to extract convex and concave object boundaries independent of the initial condition. We apply our multiscale segmentation model on a synthetic image and a medical imag

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured.EThOS - Electronic Theses Online ServiceConsejo Nacional de Ciencia y Tecnología (Mexico) (CONACYT)GBUnited Kingdo

Active meshes for motion tracking

This thesis presents an integrated approach to modelling, extraction and tracking of deformable contour meshes through image sequences, with the aim of extracting motion information about the viewed scene. The thesis begins by reviewing the area of motion estimation in computer vision, leading to a review on the formulation and initialisation of active contour models. From this review the thesis develops and provides as its major contribution an active mesh structure that may be used for motion estimation. This active mesh structure approach is combined with feature matching to provide a stable, deformable motion tracking system for real-world scenes. This system is tested on various real-world scenes and varying conditions to provide extensive and rigorous experimental proof of the validity of the formulation. Further extensions to the system are implemented, including the use of multiple and region based active meshes. Future directions of research are also suggested

Segmentação pulmonar em estudos de tomografia axial computorizada

Doutoramento em Engenharia ElectrónicaA Tomografia Axial Computorizada (TAC) é um dos meios complementares de diagnóstico médico mais eficiente no estudo das regiões pulmonares. Os métodos de segmentação pulmonar em imagens produzidas por esta modalidade são necessários sempre que se pretenda determinar áreas ou volumes, ou obter informação densitómetria; podem ainda ser integrados no pré-processamento de uma aplicação que envolva a visualização dos pulmões. No âmbito deste trabalho foram desenvolvidos três métodos de segmentação pulmonar, baseados na análise da distribuição dos níveis de cinzento da imagem de TAC da região torácica e na utilização de técnicas morfológicas, bem como no conceito de contornos activos. O método que produziu melhores resultados demonstrou ser capaz de localizar a zona de separação dos pulmões quando estes se encontram visualmente sobrepostos. Foi ainda desenvolvido um método com uma abordagem tridimensional, que processa em simultâneo a informação relativa a todas as secções do exame, e apresenta como principais vantagens a facilidade de identificação da traqueia e brônquios, bem como de múltiplas zonas de contacto entre pulmões. O desempenho como detector de contornos pulmonares, do melhor dos três primeiros métodos de segmentação desenvolvidos, e na ausência de ground truth, foi comparado com o desempenho de seis radiologistas. Também o método de segmentação tridimensional foi avaliado. Além da segmentação dos pulmões, também a segmentação de estruturas pulmonares se torna necessária quando se pretende quantificar áreas e volumes ou ainda proceder a medições densitómetrias dessas estruturas; o que pode ser utilizado no diagnóstico e seguimento de várias patologias como o enfisema bolhoso, que se caracteriza pela presença de bolhas de ar nos pulmões. Sendo assim, foram desenvolvidos três métodos de segmentação para detectar estas bolhas: o primeiro método processa os dados secção a secção e avalia a coerência longitudinal, o segundo utiliza um threshold global e operações morfológicas em 3D e o terceiro utiliza uma versão modificada do modelo deformável level-set tridimensional. Na segmentação de bolhas de ar pulmonares, que apresentam níveis de cinzento muito baixos, o ruído torna-se um problema relevante, sobretudo se as imagens corresponderem a exames de TAC de alta resolução. Assim, para atenuar o ruído das imagens, foi utilizado um conjunto de filtros e estudada a sua influência nos resultados produzidos pelos métodos de segmentação desenvolvidos. Este estudo foi realizado sobre imagens sintetizadas e imagens reais contendo bolhas naturais e artificiais a que se adicionou ruído. Finalmente, e no sentido de ilustrar a utilidade dos métodos propostos, descreve-se um exemplo de uma aplicação que permite discriminar e quantificar as zonas enfisematosas dos pulmões, bem como visualizá-las secção a secção e tridimensionalmente.X-ray Computed Tomography (CT) is one of the most efficient medical diagnosis tools and has currently a widespread usage in the study of the pulmonary region. Lung segmentation methods are necessary to compute areas or volumes or to perform densitometry; they can also be used as a pre-processing task in pulmonary visualization. In this work, three segmentation methods based on the gray level distribution analysis of CT thoracic images, morphologic techniques and active contours were developed. The method that produced best results proved to be able to locate lung boundaries even when lungs are visually superimposed on the images. Another pulmonary segmentation method using a tridimensional approach was developed; this method processes simultaneously information concerning all sections of the CT exam and has the main advantages of an easy identification of trachea and bronchi, as well as detection of multiple contact zones between lungs. Due to the lack of a ground truth, the performance of the best of the first three segmentation methods was compared with the performance of six radiologists. The tridimensional method was also evaluated. Not only the lung segmentation, but also the segmentation of pulmonary structures is needed for diagnosis and follow-up of a variety of diseases, as it is the case of the bulbous emphysema, characterized by the presence of air bubbles inside the lungs. Thus, three segmentation methods were developed to detect these bubbles, the first method processes the exam section by section and analysis longitudinal coherence, the second method uses a 3D approach with a global threshold and morphologic operations, and the third method uses a modified version of the 3D level-set deformable model. Since air bubbles have very low gray levels, noise becomes a relevant problem in their segmentation, especially in high resolution CT exams. Therefore, to reduce noise, several filters were used and their influence on the results produced by the segmentation methods was analyzed. This study was performed using synthesized and real images with natural and artificial air bubbles, to which noise was added. Finally, and to illustrate the usefulness of the proposed methods, an application was developed that allows discriminating and quantifying pulmonary emphysematous regions, as well as visualizing those regions section by section and tridimensionally.FSEPRODEP IIIFundação Calouste GulbenkianFundação Luso-Americana para o Desenvolviment

Image segmentation with variational active contours

An important branch of computer vision is image segmentation. Image segmentation aims at extracting meaningful objects lying in images either by dividing images into contiguous semantic regions, or by extracting one or more specific objects in images such as medical structures. The image segmentation task is in general very difficult to achieve since natural images are diverse, complex and the way we perceive them vary according to individuals. For more than a decade, a promising mathematical framework, based on variational models and partial differential equations, have been investigated to solve the image segmentation problem. This new approach benefits from well-established mathematical theories that allow people to analyze, understand and extend segmentation methods. Moreover, this framework is defined in a continuous setting which makes the proposed models independent with respect to the grid of digital images. This thesis proposes four new image segmentation models based on variational models and the active contours method. The active contours or snakes model is more and more used in image segmentation because it relies on solid mathematical properties and its numerical implementation uses the efficient level set method to track evolving contours. The first model defined in this dissertation proposes to determine global minimizers of the active contour/snake model. Despite of great theoretic properties, the active contours model suffers from the existence of local minima which makes the initial guess critical to get satisfactory results. We propose to couple the geodesic/geometric active contours model with the total variation functional and the Mumford-Shah functional to determine global minimizers of the snake model. It is interesting to notice that the merging of two well-known and "opposite" models of geodesic/geometric active contours, based on the detection of edges, and active contours without edges provides a global minimum to the image segmentation algorithm. The second model introduces a method that combines at the same time deterministic and statistical concepts. We define a non-parametric and non-supervised image classification model based on information theory and the shape gradient method. We show that this new segmentation model generalizes, in a conceptual way, many existing models based on active contours, statistical and information theoretic concepts such as mutual information. The third model defined in this thesis is a variational model that extracts in images objects of interest which geometric shape is given by the principal components analysis. The main interest of the proposed model is to combine the three families of active contours, based on the detection of edges, the segmentation of homogeneous regions and the integration of geometric shape prior, in order to use simultaneously the advantages of each family. Finally, the last model presents a generalization of the active contours model in scale spaces in order to extract structures at different scales of observation. The mathematical framework which allows us to define an evolution equation for active contours in scale spaces comes from string theory. This theory introduces a mathematical setting to process a manifold such as an active contour embedded in higher dimensional Riemannian spaces such as scale spaces. We thus define the energy functional and the evolution equation of the multiscale active contours model which can evolve in the most well-known scale spaces such as the linear or the curvature scale space