TEXTURE-BASED GRAPH REGULARIZATION PROCESS FOR 2D AND 3D ULTRASOUND IMAGE SEGMENTATION

In this paper, we propose to improve an unsupervised segmentation algorithm based on the graph diffusion and regularization model described by Ta in [1] by using Haralick texture features. With this framework, segmentation is performed by diffusing an indicator function over a graph representing an image. The benefit of our approach is to combine two non-local processing techniques: at pixel level with texture features and through the use of a graph structure, which allows to efficiently express relations between non-adjacent pixels. Applied on ultrasound images, and compared to a vector-valued Chan & Vese active contour, our method shows an improvement of the quality of segmentation.