Medical Image Segmentation Based on Watershed Transformation and Rough Sets

Traditional watershed algorithm often causes over-segmentation because of its high sensitivity to the weak edge and the noise. To overcome this drawback and in light of the characteristics of medical image, a new segmentation algorithm based on watershed transformation and rough set theory is proposed. The original image is partitioned into the edge-detail sub-image and smooth sub-image according to indiscernibility relation of rough set theory. Two enhancement methods are designed for the two sub-images, and watershed transformation is used for the further segmentation in the smooth sub-image. Finally, combine the two processed sub-images to obtain the segmentation result. The proposed algorithm has been executed on Magnetic Resonance Imaging (MRI) image, the analysis of compare between conventional watershed algorithm and the proposed algorithm is given. The experimental result shows that this method is efficient to restrain the over-segmentation, thus obtaining good segmentation results.