A novel method for breast mass segmentation: from superpixel to subpixel segmentation

In this paper, an effective method is proposed for breast mass segmentation using a superpixel generation and curve evolution method. The simple linear iterative clustering method and density-based spatial clustering of applications with noise method are applied to generate superpixels in mammograms at first. Thereafter, a region of interesting (ROI) that contains the breast mass is built on the superpixel generation results. Finally, the image patch and the position of the manual labeled seed are used to build the prior knowledge for the level set method driven by the local Gaussian distribution fitting energy and evolve the curve to capture the edge of breast mass in ROI. Experimental results on mammogram data set demonstrate that the proposed method shows superior performance in contrast to some well-known methods in breast mass segmentation.