Morphological watershed segmentation often leads to a serious over-se gmentation due to the noises and irregular details within an image.Watershed segmentation based on area constraint and adap ti ve gradient modification is proposed to alleviate over-segmentation and boundary bias.Firstly,the original image is tra ns formed to a morphological gradient image.In gradient relief,the high gradient amplitudes with small area are usually corres ponding to salt-like noises or bright regular details, which can be removed by the area constraint.Secondly,the function between grad ient levels and morphological structure element is established,and the viscous morphological closing operator is utilized to mod ify the relief of gradient image with different sizes of structuring elements.The effects of viscous closing applied to objects and nois e are different.for the objects regions,the sizes of structuring elements are smaller,which means they are light or less modifica tion, while for noise or details,the larger size structuring elements are employed to modify them heavily.By such an adaptive mod ification,most irregular local minimums corresponding to the low amplitudes in gradient image caused by details and nois e w ill be removed,while positions of target boundaries have less change.Finally,standard watershed transform is employed to implement segmentation.Experiments show that this method can eliminate over-segmentation effectively while preserve th e location of object contours.