Image Segmentation and Restoration Using Parametric Contours With Free Endpoints
This work addresses image processing challenges in fields like medical imaging by providing an incremental improvement to active contour methods.
The paper tackles image segmentation and restoration by introducing a novel approach for active contours with free endpoints, enabling both closed and open curves, and achieves a fast method through parametric evolution and edge-preserving denoising.
In this paper, we introduce a novel approach for active contours with free endpoints. A scheme is presented for image segmentation and restoration based on a discrete version of the Mumford-Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.