Active Contours without Edges for Vector-Valued Images

Active Contours without Edges for Vector - Valued

LBP-guided active contours

This paper investigates novel LBP-guided active contour approaches to texture segmentation. The Local Binary Pattern (LBP) operator is well suited for texture representation, combining efficiency and effectiveness for a variety of applications. In this light, two LBP-guided active contours have been formulated, namely the scalar-LBP Active Contour (s-LAC) and the vector-LBP Active Contour (v-LA...

A Level-Set and Gabor-based Active Contour Algorithm for Segmenting Textured Images

This paper applies the authors’ previously proposed vector valued active contour without edges model to segment textured images. The model uses a level set implementation and can detect edges without the use of gradient information, making it natural for use in textured image segmentation. Multiple Gabor transforms of the original image are used to discriminate textures. We show numerical resul...

Chan-Vese Segmentation

While many segmentation methods rely heavily in some way on edge detection, the “Active Contours Without Edges” method by Chan and Vese [7, 9] ignores edges completely. Instead, the method optimally fits a two-phase piecewise constant model to the given image. The segmentation boundary is represented implicitly with a level set function, which allows the segmentation to handle topological chang...

Segmentation of Bone in Clinical Knee MRI Using Texture-Based Geodesic Active Contours

This paper presents a method for automatic segmentation of the tibia and femur in clinical magnetic resonance images of knees. Texture information is incorporated into an active contours framework through the use of vector-valued geodesic snakes with local variance as a second value at each pixel, in addition to intensity. This additional information enables the system to better handle noise an...