The watershed is an efficient algorithm for the segmentation of images. However, over-segmentation, which contains so many tiny regions that regions of interest cannot be identified easily, decreases the effectiveness. In this paper, pre-processing of images and the modification of watershed algorithm are both studied to restrain the over-segmentation. In the process of pre-processing, a kind of multi-scaled transform, contrast à trous wavelet based contourlet transform, is proposed and constructed to get sparse representation. In the aspect of modifying watershed, the “texture gradient” is defined, and the texture gradient is combined with marker-based watershed algorithm to reduce the number of segmented regions. The proposed method is tested by 36 prostate MR images and compared with several image segmentation algorithms; the experiment and comparison results show that the proposed method consistently restrains the number of segmented regions. The segmentation results correctly correspond to the main tissues in the images, and each tissue is integrally segmented, respectively with the elimination of small regions. The segmentation accuracy rate is 87.29%, which is higher than other methods under comparison. 
Automatic multi-organ segmentation of prostate magnetic resonance images using watershed and nonsubsampled contourlet transform