One improved watershed transform for medical image segmentation

As a classic image segmentation method, watershed algorithm is widely used. But the over-segmentation and sensitivity to noise are its drawbacks, many improved watershed methods have been developed to solve these problems. This paper presented an improved watershed algorithm for medical image segmentation. Firstly, an iterative data-adaptive Gaussian smoother is used to smooth large scale details while suppress noises. Secondly, the contour information of the original image is enhanced and revised by fusing the gradient with the detected edges. Finally, valley-filling techenique is used to control the number of the resultant watersheded regions. The experiments have been done on the medical images and the results demonstrated the effectiveness of the proposed method.