An Improved Level Set Method on the Multiscale Edges

Abstract

The level set method can segment symmetrical or asymmetrical objects in real images according to image features. However, the segmentation performance varies with feature scale. In order to improve the segmentation effect, we propose an improved level set method on the multiscale edges, which combines the level set method with image multi-scale decomposition to form a unified model. In this model, the segmentation relies on multiscale edges, and the multiscale edges depend on multiscale decomposition. A novel total variation regularization is proposed in multiscale decomposition to preserve edges. The multiscale edges obtained by the multiscale decomposition are integrated into the segmentation process, and the object can be easily extracted from a proper scale. Experimental results indicate that this method has superior performance in precision, recall and F-measure, compared with relative edge-based segmentation methods, and is insensitive to noise and inhomogeneous sub-regions.