Image segmentation is to divide the region with special meanings into several disjoint sub-regions according to certain rules, which is the key link between image processing and image analysis. The traditional watershed image segmentation method is widely used, which has the advantages of fast and simple. However, it is easily interfered by noise, and the segmentation results are prone to lose important edge information, resulting in over-segmentation. In view of the problem of the traditional watershed image segmentation method, an improved watershed image segmentation method based on adaptive structural elements was proposed. First, the adaptive structural elements with variable shapes were constructed by using local density, symmetry, and boundary features of adjacent pixels of image targets, so as to ensure a good consistency between the proposed structural elements and the shape of image targets. Then, the adaptive structural elements were used to obtain the morphological gradient of the image, which could improve the positioning accuracy of the target edge. The L₀ norm gradient minimization and morphological open-close hybrid reconstruction were used to modify the gradient image, so as to reduce the local invalid minimum points in the gradient image and suppress the occurrence of over-segmentation. Finally, watershed segmentation was performed on the modified gradient image to realize accurate segmentation of the target region of the image. Experimental results show that the method could effectively restrain over-segmentation of traditional watershed algorithm and improve the accuracy of the target edge positioning, with high precision of image segmentation.