Watershed transformation is a powerful morphological operator for image segmentation.It is performed on the gradient of the image to be segmented.Each minimum of the gradient leads to a region in the resulting segmentation.However

conventional gradient operators generally produce many local minima. Produced by noise and quantization error. Experimental result

which are caused by noise or quantization error.Hence

watershed transformation with a conventional gradient operator usually results in over segmentation.To alleviate this problem

this paper presents a multiscale algorithm for computing morphological gradient images

with effective handling of both step and blurred edges.We also present an algorithm to eliminate the local minima produced by noise and quantization error. Experimental results demonstrate that the proposed algorithm can effectively enhance step and blurred edges and reduce the number of local minima.Watershed transformation with the proposed algorithm produces meaningful segmentations

even without a region merging step.The proposed algorithm can significantly reduce the computational load of watershed based image segmentation methods.