Efficient Shape Priors for Spline-Based Snakes

Parametric active contours are an attractive approach for image segmentation, thanks to their computational efficiency. They are driven by application-dependent energies that reflect the prior knowledge on the object to be segmented. We propose an energy involving shape priors acting in a regularization-like manner. Thereby, the shape of the snake is orthogonally projected onto the space that spans the affine transformations of a given shape prior. The formulation of the curves is continuous, which provides computational benefits when compared with landmark-based (discrete) methods. We show that this approach improves the robustness and quality of spline-based segmentation algorithms, while its computational overhead is negligible. An interactive and ready-to-use implementation of the proposed algorithm is available and was successfully tested on real data in order to segment Drosophila flies and yeast cells in microscopic images

Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain

We provide a generic framework to learn shape dictionaries of landmark-based curves that are defined in the continuous domain. We first present an unbiased alignment method that involves the construction of a mean shape as well as training sets whose elements are subspaces that contain all affine transformations of the training samples. The alignment relies on orthogonal projection operators that have a closed form. We then present algorithms to learn shape dictionaries according to the structure of the data that needs to be encoded: 1) projection-based functional principal-component analysis for homogeneous data and 2) continuous-domain sparse shape encoding to learn dictionaries that contain imbalanced data, outliers, or different types of shape structures. Through parametric spline curves, we provide a detailed and exact implementation of our method. We demonstrate that it requires fewer parameters than purely discrete methods and that it is computationally more efficient and accurate. We illustrate the use of our framework for dictionary learning of structures in biomedical images as well as for shape analysis in bioimaging

B-Spline Active Contour with Handling of Topology Changes for Fast Video Segmentation

This paper deals with video segmentation for MPEG-4 and MPEG-7 applications. Region-based active contour is a powerful technique for segmentation. However most of these methods are implemented using level sets. Although level-set methods provide accurate segmentation, they suffer from large computational cost. We propose to use a regular B-spline parametric method to provide a fast and accurate segmentation. Our B-spline interpolation is based on a fixed number of points 2j depending on the level of the desired details. Through this spatial multiresolution approach, the computational cost of the segmentation is reduced. We introduce a length penalty. This results in improving both smoothness and accuracy. Then we show some experiments on real-video sequences

Biomarqueurs de la morphologie du cortex cérébral par imagerie par résonance magnétique (IRM) anatomique : application à la maladie d'Alzheimer

Les modifications de la morphologie du cortex cérébral induites par la maladie d'Alzheimer à ses stades précoces contribuent à l'intérêt croissant à l'égard des biomarqueurs de la morphologie corticale. Ceux-ci permettraient notamment une meilleure compréhension de l'impact de cette pathologie sur l'anatomie cérébrale et une détection plus précoce de la maladie. L'originalité de notre travail par rapport au reste de la littérature est de s'intéresser à la morphologie des surfaces interne (interface substance blanche / substance grise) et externe (interface substance grise / liquide cérébro-spinal) du cortex cérébral. Dans cette perspective, nous avons développé des méthodes d'estimation de la courbure et de la dimension fractale des surfaces corticales. A partir de ces biomarqueurs morphologiques et de l'épaisseur corticale dont la méthode d'estimation a été précédemment développée dans le laboratoire, nous avons exploré l'impact de la maladie d'Alzheimer sur la morphologie du manteau cortical et nous avons évalué leur apport individuel et celui de leur association au diagnostic précoce de la maladie. Nos résultats montrent une influence significative de la pathologie sur la morphologie des sillons et sur celle des circonvolutions des surfaces corticales interne et externe. En termes d'application diagnostique, nous montrons que prises isolément, l'épaisseur corticale présente une meilleure capacité prédictive que la courbure corticale, nous ne constatons en revanche aucune capacité prédictive de la dimension fractale. Par contre, nous montrons que l'utilisation conjointe de l'épaisseur corticale et de la courbure permet une amélioration significative du diagnostic précoce.Morphological alterations of the cortical mantle in early stage of Alzheimer's disease have led to an increasing interest towards morphological biomarkers of the cerebral cortex. By providing a quantitative measure of the cortical shape, morphological biomarkers could provide better understanding of the impact of the disease on the cortical anatomy and play a role in early diagnosis. Therefore, as a primary goal in this study, we developed cortical surface curvature and fractal dimension estimation methods. We then applied those methods, together with the estimation of cortical thickness, to investigate the impact of Alzheimer's disease on the cortical shape as well as the contribution of cortical thickness and cortical curvature to the early diagnosis of Alzheimer's disease. The originality of this work lies in the estimation of sulcal and gyral curvature of the internal (gray matter/white matter boundary) and external (gray matter/cerebrospinal fluid boundary) cortical surfaces in addition to the fractal dimensions of these boundaries. Our results showed significant impact of Alzheimer's disease on sulcal and gyral shapes of the internal and external cortical surfaces. In addition, cortical thickness was found to have better ability than cortical curvature for the early diagnosis of Alzheimer's disease; no significant ability for the early diagnosis was found using fractal dimension. However, we found significant improvement in early diagnosis by combining cortical thickness and cortical curvature