Region-based approach for discriminant snakes

This paper proposes a statistic framework for segmenting textured areas over real images by discriminant snakes. Our active contour model has the ability to learn different texture prototypes and generate a global statistical model from a multi-valued function. This function is generated by means of filter responses over the texture regions. Linear discriminant analysis is performed to obtain a statistical classifier embodied into the snake scheme. Given an input image composed by different texture types, a likelihood map is built and the discriminant snake deforms on it to delineate regions with similar texture descriptions according to the learned texture patterns. Our method is tested on two different image applications: aerial images and medical (ultrasound) images, and the results are very encouraging.