Texture image segmentation using statistical active contours

We present texture-based active contours method for two-phase image segmentation in a statistical framework. The proposed method first combines color, texture, and saliency weight to form an augmented image and introduces the joint distribution of these features into the image likelihood term in the energy function. Second, we use the local probability distribution to obtain a smooth label that can reduce the fragmentation in the initialization and evolution of segmentation contours. Finally, we propose a simple and efficient geometric prior based directly on the level sets and introduce the related spatial constraints into the Bayes inference to estimate the smooth probabilistic label. Therefore, the image is represented by high-dimensional features but segmented in low-dimensional space. Furthermore, evolving of the level-set function and updating of the smooth probabilistic label are run alternately in a fast manner. We experimentally compare our texture-based method with others on complicated natural images and demonstrate its good performance in practice. (C) 2018 SPIE and IS&T