Variational and Shape Prior-based Level Set Model for Image Segmentation

International audienceA new image segmentation model based on level sets approach is presented herein. We deal with radiographic medical images where boundaries are not salient, and objects of interest have the same gray level as other structures in the image. Thus, an a priori information about the shape we look for is integrated in the level set evolution for good segmentation results. The proposed model also accounts a penalization term that forces the level set to be close to a signed distance function (SDF), which then avoids the re-initialization procedure. In addition, a variant and complete Mumford-Shah model is used in our functional; the added Hausdorff measure helps to better handle zones where boundaries are occluded or not salient. Finally, a weighted area term is added to the functional to make the level set drive rapidly to object's boundaries. The segmentation model is formulated in a variational framework, which, thanks to calculus of variations, yields to partial differential equations (PDEs) to guide the level set evolution. Results obtained on both synthetic and digital radiographs reconstruction (DRR) show that the proposed model improves on existing prior and non-prior shape based image segmentation

Contraintes de forme géométriques pour les contours actifs orientés région

Nous présentons une nouvelle approche permettant d'incorporer un a priori de forme dans des contours actifs orientés région afin d'améliorer leur robustesse en présence de bruit texturé et d'occultations partielles. Nous définissons un descripteur de forme construit à partir des moments de Legendre. Celui-ci est introduit dans un schéma général de dérivation proposé récemment, qui permet d'établir l'équation d'évolution d'un contour actif minimisant la distance quadratique entre les moments de la région définie par le contour et ceux d'une forme de référence. Nous montrons les capacités de notre a priori à contraindre une courbe pour qu'elle prenne une forme prédéfinie. Enfin nous introduisons notre descripteur dans une fonctionnelle de segmentation à deux classes et montrons son intérêt en présence d'occultation et de bruit texturé

A PDE Method to Segment Image Linear Objects with Application to Lens Distortion Removal

In this paper, we propose a partial differential equation based method to segment image objects, which have a given parametric shape based on energy functional. The energy functional is composed of a term that detects object boundaries and a term that constrains the contour to find a shape compatible with the parametric shape. While the shape constraints guiding the PDE may be determined from object's shape statistical models, we demonstrate the proposed approach on the extraction of objects with explicit shape parameterization, such as linear image segments. Several experiments are reported on synthetic and real images to evaluate our approach. We also demonstrate the successful application of the proposed method to the problem of removing camera lens distortion, which can be significant in medium to wide-angle lenses

A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.894244Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters (PFs) have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter. The PF also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and backgroun

Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images

A new algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the hipoechogenic characteristics of the lumen for the identification of the carotid boundaries and the echogenic characteristics for the identification of the bifurcation boundaries. The image to be segmented is processed with the application of an anisotropic diffusion filter for speckle removal and morphologic operators are employed in the detection of the artery. The obtained information is then used in the definition of two initial contours, one corresponding to the lumen and the other to the bifurcation boundaries, for the posterior application of the Chan-vese level set segmentation model. A set of longitudinal B-mode images of the common carotid artery (CCA) was acquired with a GE Healthcare Vivid-e ultrasound system (GE Healthcare, United Kingdom). All the acquired images include a part of the CCA and of the bifurcation that separates the CCA into the internal and external carotid arteries. In order to achieve the uppermost robustness in the imaging acquisition process, i.e., images with high contrast and low speckle noise, the scanner was adjusted differently for each acquisition and according to the medical exam. The obtained results prove that we were able to successfully apply a carotid segmentation technique based on cervical ultrasonography. The main advantage of the new segmentation method relies on the automatic identification of the carotid lumen, overcoming the limitations of the traditional methods