We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and combines aspects of snakes/balloons and region growing. Indeed the classic snakes/balloons and region growing algorithms can be directly derived from our approach. We provide theoretical analysis of region competition including accuracy of boundary location, criteria for initial conditions, and the relationship to edge detection using filters. It is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on grey level images, color images and texture images. The novel color model allows us to eliminate intensity gradients and shadows, thereby obtaining segmentation based on the albedos of objects. It also helps detect highlight regions. 1 Division of Appli...

This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, includingsegmentation, shape representation, matching, and motion tracking.

This paper presents a topologically adaptable snakes model for image segmentation and object representation. The model is embedded in the framework of domain subdivision using simplicial decomposition. This framework extends the geometric and topological adaptability of snakes while retaining all of the features of traditionalsnakes, such as user interaction, and overcoming many of the limitations of traditionalsnakes. By superposing a simplicial grid over the image domain and using this grid to iteratively reparameterize the deforming snakes model, the model is able to flow into complex shapes, even shapes with significant protrusions or branches, and to dynamically change topology as necessitated by the data. Snakes can be created and can split into multiple parts or seamlessly merge into other snakes. The model can also be easily converted to and from the traditional parametric snakes model representation. We apply a 2D model to various synthetic and real images in order to segment ...

AbstractÐA method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions obtained via any region segmentation algorithm, e.g., texture, color, or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported. Index TermsÐImage segmentation, region merging, object detection and recognition, deformable templates, nonrigid shape models, statistical shape models. 1

This paper describes a new approach to adaptive estimation of parametric deformable contours based on B-spline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region -based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion. The result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequacy and good performance of the approach.

Precise segmentation of underlying objects in an image is very important especially for biomedical image analysis. Here, we present an integrated approach for boundary finding using region and curvature information along with the gradient. Unlike the previous methods, where smoothing is enforced by penalizing curvature, here the grey level curvature is used as an extra source of information. However, information fusion may not be useful unless used properly. To address that, we present results that highlight the pros and cons of using the various sources of information and indicate when one should get precedence over the others. Keywords: biomedical image analysis, image segmentation, boundary finding, region based segmentation, curvature 1 Introduction Segmentation and analysis of underlying structures in an image is of importance in a variety of image analysis and computer vision applications including robot vision, pattern recognition and biomedical image processing. We are espec...

In this paper, we propose a new approach for tracking the deformation of the Left Ventricular (LV) myocardium from two-dimensional Magnetic Resonance (MR) phase contrast velocity fields. The use of phase contrast MR velocity data in cardiac motion problems has been introduced by others [1] and shown to be potentially useful for tracking discrete tissue elements, and therefore characterizing LV motion. However, we show here that these velocity data i.) are extremely noisy near the LV borders and ii.) cannot alone be used to estimate the motion and the deformation of the entire myocardium due to noise in the velocity fields. In this new approach, we use the natural spatial constraints of the endocardial and epicardial contours, detected semi-automatically in each image frame, to help remove noisy velocity vectors at the LV contours. The information from both the boundaries and the phase contrast velocity data is then integrated into a deforming mesh that is placed over the myocardium at ...

. This work proposes a unified framework to track the deformation of the myocardium using velocity fields and boundary information. The deformation of the myocardium is characterized by a deforming mesh. A general framework for locally regularizing the velocity field has been developed. The tracking is modeled as an estimation problem which makes it possible to properly take into account uncertainties in the velocity and boundary measurements. The results of experiments conducted on phantom data and on in vivo data demonstrate the strength of the approach. Keywords: non-rigid motion, phase contrast MRI, tracking, left ventricular motion 1. Introduction The ability to analyze and quantize the internal deformation of the myocardium from a sequence of images is of fundamental importance for the diagnosis of heart disease. A deformed body can be described by its boundary, and recent efforts have been aimed at understanding the complex motion of contours and surfaces [5, 13]. However, as o...

Figure-Ground segmentation is a fundamental problem in computer vision. The main difficulty is the integration of low-level, pixel-based local image features to obtain global objectbased descriptions. Active contours in the form of snakes, balloons, and level-set modeling techniques have been proposed that satisfactorily address this question for certain applications. However, these methods require manual initialization, do not always perform well near sharp protrusions or indentations, or often cross gaps. We propose an approach inspired by these methods and a shock-based representation of shape in terms of parts, protrusions, and bends. In this representation parts are related to fourth order shocks. Since initially it is not clear where the objects or their parts are, parts are hypothesized in the form of fourth order shocks randomly initialized in homogeneous areas of images. These shocks then form evolving contours, or bubbles, which grow, shrink, merge, split and disappear to cap...

This paper describes a new method for automatic estimation of the contours of the femur and of the cranial cross-section in fetal ultrasound images. Our approach can be described as a regionbased maximum likelihood formulation of parametric deformable contours. This formulation provides robustness against the poor image quality, and allows simultaneous estimation of the contour parameters together with other parameters of the model. Implementation is carried out by a deterministic iterative algorithm with minimal user intervention. Experimental results testify for the very good performance of the approach.