Research on image segmentation algorithms combining gradient field and variational methods

Abstract

Image segmentation, as an important direction of computer vision, is gradually being applied to a variety of fields, however, the existing image segmentation methods still need to be improved in terms of segmentation accuracy and effect. In this paper, the variational level set method is used as the level set image segmentation method, and its theoretical basics and solution method (gradient descent flow method) are described in detail. For the problem of insufficient gradient vector flow in the traditional parametric active contour Sanke model, a global gradient vector flow model that can overcome the noise interference is given to obtain a more accurate gradient field, thus combining with the variational level set method to build an image segmentation model based on global gradient vector flow (GGF Snake). In the comparison experiments with three commonly used image segmentation algorithms, the DSC value of this paper’s algorithm reaches more than 96.00%, and the time used is less than 15s, which is better than the remaining three algorithms, and verifies the superiority of this paper’s algorithm.