An Intensity-Texture model based level set method for image segmentation

Abstract

In this paper, a novel level set segmentation model integrating the intensity and texture terms is proposed to segment complicated two-phase nature images. Firstly, an intensity term based on the global division algorithm is proposed, which can better capture intensity information of image than the Chan–Vese model (CV). Particularly, the CV model is a special case of the proposed intensity term under a certain condition. Secondly, a texture term based on the adaptive scale local variation degree (ASLVD) algorithm is proposed. The ASLVD algorithm adaptively incorporates the amplitude and frequency components of local intensity variation, thus, it can extract the non-stationary texture feature accurately. Finally, the intensity term and the texture term are jointly incorporated into level set and used to construct effective image segmentation model named as the Intensity-Texture model. Since the intensity term and the texture term are complementary for image segmentation, the Intensity-Texture model has strong ability to accurately segment those complicated two-phase nature images. Experimental results demonstrate the effectiveness of the proposed Intensity-Texture model.