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.