A new selective segmentation model for texture images and applications to medical images

Abstract Segmentation of texture images is an active area of image processing. Selective segmentation is the process of extracting a region of interest (ROI) in the image. In this paper, we propose a new model for selective segmentation of texture images, by incorporating geometrical constraints after smoothing the texture in image. The proposed model uses L 0 norm for smoothing of the texture and Badshah–Chen energy with local Gaussian kernel data fitting for selective segmentation. The proposed model is minimized to get gradient descent through Euler Lagrange's equation, which is then discretized through finite differences and the corresponding difference equation is solved by using additive operator splitting method. Experimental results of the proposed model are compared with the existing selective segmentation models and related models which are not based on selective segmentation. The model is further tested on medical images.