Breast MR image segmentation is difficult because of complex organization and intensity inhomogeneity. This paper proposes a segmentation method based on dual-tree complex wavelet transform and density clustering. Firstly

the image is denoised by using complex wavelet domain bivariate model combined with anisotropic diffusion function; Then simple linear iterative clustering (SLIC) algorithm is used to obtain the neighbors of each superpixel

thereby reducing the time of searching for the nearest neighbor of each sample in KNN-DPC algorithm. Finally

nearest neighbor sample density information of superpixel region is introduced

and distribution strategies from KNN-DPC algorithm are used for adaptive clustering. The segmentation results of simulation and clinical data show that the proposed algorithm can segment breast MR images effectively.