introduction: nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. one of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis.  materials and methods: in this work, a hybrid method based on geometric deformable models...

Introduction: Nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. One of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis.  Materials and Methods: In this work, a hybrid method based on Geometric Deformable Models...

Active contour models are an efficient, accurate, and robust tool for the segmentation of 2D and 3D image data. In particular, geometric deformable models (GDM) that represent an active contour as the level set of an implicit function have proven to be very effective. GDMs, however, do not provide any topology control, i.e. contours may merge or split arbitrarily and hence change the genus of t...

Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models — the ability to ...

Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models implemented using level set methods have advantages over parametric models due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models — the ability to automati...

Deformable shape models require correspondence across the training population in order to generate a statistical model for use as a future geometric prior. Traditional methods use fixed sampling and assume correspondence, or attempt to induce correspondence by minimizing variance. In this paper, we define a training methodology for sampled medial deformable shape models (m-reps) which generates...

Geometric deformable models based on the level set method have become very popular in the last several years. To overcome an inherent limitation in accuracy while maintaining computational efficiency, adaptive grid techniques using local grid refinement have been developed for use with these models. This strategy, however, requires a very complex data structure, yields large numbers of contour ...

Deformable models” refers to a class of physics-based modeling methods with an extensive track record in computer vision, medical imaging, computer graphics, geometric design, and related areas. Unlike the Eulerian (fluid) formulations associated with level set methods, deformable models are characterized by Lagrangian (solid) formulations, three variants of which are reviewed herein.