Texture-based graph regularization process for 2D and 3D ultrasound image segmentation
Cyrille Faucheux, Julien Olivier, Romuald Boné, Pascal Makris · 2012
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.