An improved image segmentation approach based on level set and mathematical morphology

Level set methods offer a powerful approach for the medical image segmentation since it can handle any of the cavities, concavities, convolution, splitting or merging. However, this method requires specifying initial curves and can only provide good results if these curves are placed near symmetrically with respect to the object boundary. Another well known segmentation technique--morphological watershed transform can segment unique boundaries from an image, but it is very sensitive to small variations of the image magnitude and consequently the number of generated regions is undesirably large and the segmented boundaries is not smooth enough. In this paper, a hybrid 3D medical image segmentation algorithm, which combines the watershed transform and level set techniques, is proposed. This hybrid algorithm resolves the weaknesses of each method. An initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude, then this segmentation results is treated as the initial localization of the desired contour, and used in the following level set method, which provides closed, smoothed and accurately localized contours or surfaces. Experimental results are also presented and discussed.