Oriented distance regularized level set evolution for image segmentation

Abstract

The conventional distance regularized level set evolution method has been very popular in image segmentation, but usually it cannot converge to the desired boundary when there are multiple and unwanted boundaries in the image. By observation, the gradient direction between the target boundaries and the unwanted boundaries are usually different in one image. The gradient direction information of the boundaries can guide the orientation of the level set function evolution. In this study, the authors improved the conventional distance regularized level set evolution method, introduced new edge indicator functions and proposed an oriented distance regularized level set evolution method for image segmentation. The experiment results show the proposed method has a better segmentation result in images with multiple boundaries. Moreover, alternately selecting the edge indicator functions we proposed during the level set evolution can lead the zero level set contour to converge to the desired boundaries in complicated images.

Adaptive segmentation algorithm based on level set model in medical imaging

“…To overcome these disadvantages, several methods are proposed for example paper [16] demonstrat, both theoretically and experimentally, that indirect regularization has some advantages over direct regularization, Yu et al [17] suggest novel active contour model (R-DRLSE model) for image segmentation and Young et al [18] develop a new approach to contour evolution. Liu and Xu [19] propose oriented distance regularized level evolution and Cai [20] propose a coupled model for image segmentation and restoration.…”