Segmentation of breast MR images based on multiresolution level set algorithm

Abstract

This paper proposes a novel multiresolution level set algorithm to segment breast MR images, which have a large amount of information, intensity inhomogeneities, and weak boundary. The core of the algorithm is to get the coarse scale image by analyzing the image in multi-scale space with wavelet multiscale decomposition. Then, to segment the analysed results in terms of improved CV model. In order to deal with the effect of bias field on the global images, the algorithm introduces a local fitting term into the improved CV model and optimizes the coarse-scale segmentation result by using the Kernel function to further improve the CV model. Experimental results on both synthetic and real breast MR images demonstrate that the proposed algorithm can segment the images with intensity inhomogeneity effectively and efficiently, also it can segment the images far more accurately, computationally efficiently, and much less sensitively to the initial contour.