Image Segmentation Algorithm using Watershed Transform and Level Set Method

A novel method for image segmentation is proposed in this paper, which combines the watershed transform and region-based level set method. The watershed transform is first used to presegment the image so as to get the initial partition of it. Some useful information of the primitive regions and boundaries can be obtained. The region-based level set method is then applied for extracting the boundaries of objects on the basis of the presegmentation. The consumed time does not depend on the size of the image but the number of presegmented regions because only label level set function is updated instead of the level set function for each pixel. Therefore, the proposed method is computationally efficient. Moreover, the algorithm can localize the boundary of the regions exactly due to the edges obtained by the watersheds. The efficiency and accuracy of the algorithm is demonstrated by the experiments on the MR brain images.

1. INTRODUCTION

Image segmentation is one of the most difficult problems in the field of image processing and computer vision. Extracting certain structure from others is very popular nowadays especially in medical image analysis. It is very important for the diagnosis of diseases and clinical operations. Researchers have done great efforts to improve the performance of the segmentation algorithms.