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.

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 object-based 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. 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 capture the objects in the image. In the homogeneous areas of the i...

Even though methods based on the use of deformable models have become prevalent, the quality of their output depends critically on the model's initial state. The issue of initializing such models, however, has not received much attention even though it is often key to the implementation of a truly useful system. We therefore present a new approach to segmentation of 3-Dimensional shapes that initializes and then optimizes a 3-D surface model given only the data and a very small number of 3-D seed points and corresponding surface normals. This is a valuable capability for medical, robotic and cartographic applications where such seed points can be naturally supplied. In effect, the surface model is clamped onto the object boundary in manner reminiscent of a Velcro being closed. We develop the method's mathematic framework and show results using volumetric medical data.

Volume segmentation is an important part of computer based medical applications for diagnosis and analysis of anatomical data. With rapid advances in medical imaging modalities and volume visualization techniques, computer based diagnosis is fast becoming a reality. These computer based tools allow scientists and physicians to understand and diagnose anatomical structures by virtually interacting with them. Volume segmentation plays a critical role by facilitating automatic or semiautomatic extraction of the anatomical organ or region-of-interest. In this review, we provide an introduction to various segmentation algorithms found in the literature. We classify the algorithms into three categories: structural techniques, statistical techniques and hybrid techniques. Under structural techniques we will review algorithms which take into consideration structural information for segmentation. Stochastic techniques are those which perform segmentation based on statistical analysis methods and under hybrid techniques we will review algorithms which make use