Brain Structure Segmentation from MRI by Geometric Surface Flow

We present a method for semiautomatic segmentation of brain structures such as thalamus from MRI images based on the concept of geometric surface flow. Given an MRI image, the user can interactively initialize a seed model within region of interest. The model will then start to evolve by incorporating both boundary and region information following the principle of variational analysis. The deformation will stop when an equilibrium state is achieved. To overcome the low contrast of the original image data, a nonparametric kernel-based method is applied to simultaneously update the interior probability distribution during the model evolution. Our experiments on both 2D and 3D image data demonstrate that the new method is robust to image noise and inhomogeneity and will not leak from spurious edge gaps

On using an analogy to heat flow for shape extraction

We introduce a novel evolution-based segmentationalgorithm which uses the heat flow analogy togain practical advantage. The proposed algorithm consistsof two parts. In the first part, we represent a particular heatconduction problem in the image domain to roughly segmentthe region of interest. Then we use geometric heatflow to complete the segmentation, by smoothing extractedboundaries and removing noise inside the prior segmentedregion. The proposed algorithm is compared with activecontour models and is tested on synthetic and medicalimages. Experimental results indicate that our approachworks well in noisy conditions without pre-processing. Itcan detect multiple objects simultaneously. It is alsocomputationally more efficient and easier to control andimplement in comparison with active contour models

Interactive Segmentation of 3D Medical Images with Implicit Surfaces

To cope with a variety of clinical applications, research in medical image processing has led to a large spectrum of segmentation techniques that extract anatomical structures from volumetric data acquired with 3D imaging modalities. Despite continuing advances in mathematical models for automatic segmentation, many medical practitioners still rely on 2D manual delineation, due to the lack of intuitive semi-automatic tools in 3D. In this thesis, we propose a methodology and associated numerical schemes enabling the development of 3D image segmentation tools that are reliable, fast and interactive. These properties are key factors for clinical acceptance. Our approach derives from the framework of variational methods: segmentation is obtained by solving an optimization problem that translates the expected properties of target objects in mathematical terms. Such variational methods involve three essential components that constitute our main research axes: an objective criterion, a shape representation and an optional set of constraints. As objective criterion, we propose a unified formulation that extends existing homogeneity measures in order to model the spatial variations of statistical properties that are frequently encountered in medical images, without compromising efficiency. Within this formulation, we explore several shape representations based on implicit surfaces with the objective to cover a broad range of typical anatomical structures. Firstly, to model tubular shapes in vascular imaging, we introduce convolution surfaces in the variational context of image segmentation. Secondly, compact shapes such as lesions are described with a new representation that generalizes Radial Basis Functions with non-Euclidean distances, which enables the design of basis functions that naturally align with salient image features. Finally, we estimate geometric non-rigid deformations of prior templates to recover structures that have a predictable shape such as whole organs. Interactivity is ensured by restricting admissible solutions with additional constraints. Translating user input into constraints on the sign of the implicit representation at prescribed points in the image leads us to consider inequality-constrained optimization

GEOMETRIC ANALYSIS TOOLS FOR MESH SEGMENTATION

Surface segmentation, a process which divides a surface into parts, is the basis for many surface manipulation applications which include model metamorphosis, model simplifica- tion, model retrieval, model alignment and texture mapping. This dissertation discusses novel methods for geometric surface analysis and segmentation and applications for these methods. Novel work within this dissertation includes a new 3D mesh segmentation algo- rithm which is referred to as the ridge-walking algorithm. The main benefit of this algo- rithm is that it can dynamically change the criteria it uses to identify surface parts which allows the algorithm to be adjusted to suit different types of surfaces and different segmen- tation goals. The dynamic segmentation behavior allows users to extract three different types of surface regions: (1) regions delineated by convex ridges, (2) regions delineated by concave valleys, and (3) regions delineated by both concave and convex curves. The ridge walking algorithm is quantitatively evaluated by comparing it with competing algo- rithms and human-generated segmentations. The evaluation is accompanied with a detailed geometrical analysis of a select subset of segmentation results to facilitate a better under- standing of the strengths and weaknesses of this algorithm. The ridge walking algorithm is applied to three domain-specific segmentation prob- lems. The first application uses this algorithm to partition bone fragment surfaces into three semantic parts: (1) the fracture surface, (2) the periosteal surface and (3) the articular surface. Segmentation of bone fragments is an important computational step necessary in developing quantitative methods for bone fracture analysis and for creating computational tools for virtual fracture reconstruction. The second application modifies the 3D ridge walking algorithm so that it can be applied to 2D images. In this case, the 2D image is modeled as a Monge patch and principal curvatures of the intensity surface are computed iv for each image pixel. These principal curvatures are then used by ridge walking algorithm to segment the image into meaningful parts. The third application uses the ridge walking algorithm to facilitate analysis of virtual 3D terrain models. Specifically, the algorithm is integrated as a part of a larger software system designed to enable users to browse, visualize and analyze 3D geometric data generated by NASA’s Mars Exploratory Rovers Spirit and Opportunity. In this context, the ridge walking algorithm is used to identify surface features such as rocks in the terrain models

Análise funcional do ventrículo esquerdo em angio-TC coronária

Doutoramento em Engenharia InformáticaCoronary CT angiography is widely used in clinical practice for the assessment of coronary artery disease. Several studies have shown that the same exam can also be used to assess left ventricle (LV) function. LV function is usually evaluated using just the data from end-systolic and end-diastolic phases even though coronary CT angiography (CTA) provides data concerning multiple cardiac phases, along the cardiac cycle. This unused wealth of data, mostly due to its complexity and the lack of proper tools, has still to be explored in order to assess if further insight is possible regarding regional LV functional analysis. Furthermore, different parameters can be computed to characterize LV function and while some are well known by clinicians others still need to be evaluated concerning their value in clinical scenarios. The work presented in this thesis covers two steps towards extended use of CTA data: LV segmentation and functional analysis. A new semi-automatic segmentation method is presented to obtain LV data for all cardiac phases available in a CTA exam and a 3D editing tool was designed to allow users to fine tune the segmentations. Regarding segmentation evaluation, a methodology is proposed in order to help choose the similarity metrics to be used to compare segmentations. This methodology allows the detection of redundant measures that can be discarded. The evaluation was performed with the help of three experienced radiographers yielding low intraand inter-observer variability. In order to allow exploring the segmented data, several parameters characterizing global and regional LV function are computed for the available cardiac phases. The data thus obtained is shown using a set of visualizations allowing synchronized visual exploration. The main purpose is to provide means for clinicians to explore the data and gather insight over their meaning, as well as their correlation with each other and with diagnosis outcomes. Finally, an interactive method is proposed to help clinicians assess myocardial perfusion by providing automatic assignment of lesions, detected by clinicians, to a myocardial segment. This new approach has obtained positive feedback from clinicians and is not only an improvement over their current assessment method but also an important first step towards systematic validation of automatic myocardial perfusion assessment measures.A angiografia coronária por TC (angio-TC) é prática clínica corrente para a avaliação de doença coronária. Alguns estudos mostram que é também possível utilizar o exame de angio-TC para avaliar a função do ventrículo esquerdo (VE). A função ventricular esquerda (FVE) é normalmente avaliada considerando as fases de fim de sístole e de fim de diástole, apesar de a angio-TC proporcionar dados relativos a diferentes fases distribuídas ao longo do ciclo cardíaco. Estes dados não considerados, devido à sua complexidade e à falta de ferramentas apropriadas para o efeito, têm ainda de ser explorados para que se perceba se possibilitam uma melhor compreensão da FVE. Para além disso, podem ser calculados diferentes parâmetros para caracterizar a FVE e, enquanto alguns são bem conhecidos dos médicos, outros requerem ainda uma avaliação do seu valor clínico. No âmbito de uma utilização alargada dos dados proporcionados pelos angio- TC, este trabalho apresenta contributos ao nível da segmentação do VE e da sua análise funcional. É proposto um método semi-automático para a segmentação do VE de forma a obter dados para as diferentes fases cardíacas presentes no exame de angio- TC. Foi também desenvolvida uma ferramenta de edição 3D que permite aos utilizadores a correcção das segmentações assim obtidas. Para a avaliação do método de segmentação apresentado foi proposta uma metodologia que permite a detecção de medidas de similaridade redundantes, a usar no âmbito da avaliação para comparação entre segmentações, para que tais medidas redundantes possam ser descartadas. A avaliação foi executada com a colaboração de três técnicos de radiologia experientes, tendo-se verificado uma baixa variabilidade intra- e inter-observador. De forma a permitir explorar os dados segmentados, foram calculados vários parâmetros para caracterização global e regional da FVE, para as diversas fases cardíacas disponíveis. Os resultados assim obtidos são apresentados usando um conjunto de visualizações que permitem uma exploração visual sincronizada dos mesmos. O principal objectivo é proporcionar ao médico a exploração dos resultados obtidos para os diferentes parâmetros, de modo a que este tenha uma compreensão acrescida sobre o seu significado clínico, assim como sobre a correlação existente entre diferentes parâmetros e entre estes e o diagnóstico. Finalmente, foi proposto um método interactivo para ajudar os médicos durante a avaliação da perfusão do miocárdio, que atribui automaticamente as lesões detectadas pelo médico ao respectivo segmento do miocárdio. Este novo método obteve uma boa receptividade e constitui não só uma melhoria em relação ao método tradicional mas é também um primeiro passo para a validação sistemática de medidas automáticas da perfusão do miocárdio

Automatic quantification of brain midline shift in CT images

Ph.DDOCTOR OF PHILOSOPH