Adaptive Distance Preserving Level Set Evolution for Image Segmentation

Abstract

The distance preserving level set method proposed by Li et al. has many advantages over the traditional variational level set methods. However, it has the disadvantage of requiring the initial curve to surround (let in or keep out) the objects to be detected. In this paper, an adaptive distance preserving level set method is proposed, in which the initial curve is no longer required to surround (let in or keep out) the objects to be detected, i.e., the initial curve can be anywhere in the image. The proposed method can detect certain object boundaries, for which the original method is not applicable. E.g. it can automatically detect interior and exterior contours of an object and edges of multi-objects, starting with only one initial curve whose position is anywhere in the image. Moreover, active contours can move into boundary concavities and perform better in the presence of weak boundaries. The proposed method has been applied to synthetic and real images of different object boundaries with promising results.