A parameter-automatically-optimized graph-based segmentation method for breast tumors in ultrasound images

This paper introduces a parameter-automatically-optimized robust graph-based image segmentation method (PAORGB) for segmenting breast tumors in ultrasonic images. The robust graph-based (RGB) segmentation algorithm is based on the minimum spanning trees in a graph generated from an image. However, the values of k and α, which are two significant parameters in the RGB algorithm, are empirically selected in the reported studies. In this paper, we propose the PAORGB method, based on the particle swarm optimization algorithm to suitably set k and α, so as to overcome the problem of under-segmentation or over-segmentation in the RGB segmentation algorithm. Experimental results have shown that the proposed segmentation algorithm can successfully and more accurately detect tumors and extract lesions in ultrasound images in comparison with the RGB with default parameter settings and the Fuzzy C means clustering.