An improved watershed in the medical image segmentation based on the bi-dimensional ensemble empirical mode decomposition

In the brain MR image segmentation applied the watershed algorithm, there are some problems like the segmented precision is affected by the noise easily and edges are blurred. In order to solving these problems, we improve the watershed algorithm based on the bi-dimensional ensemble empirical mode decomposition (BEEMD), and apply the improved algorithm to the brain MR image segmentation. In this paper, the BEEMD is used to decompose the MR images into multiple BIMFs with different frequency. For different segmented objects, these BIMFs are given different weights to enhance the details of the various regions. Then, the enhanced image is segmented by applying improved watershed algorithm. Finally, we correct the segmentation results and fix their blurring details. Our method improves the over-segmentation problem of watershed and the details blurred problem of the improved watershed. In addition, this method has good robustness to different MR images.