Image Segmentation and Restoration Using Parametric Contours With Free Endpoints

TL;DR

This paper presents a new parametric active contour method with free endpoints for image segmentation and restoration, incorporating both normal and tangential endpoint flows, suitable for open and closed curves.

Contribution

It introduces a novel active contour model with free endpoints, combining normal and tangential flows, and applies a parametric approach with edge-preserving denoising for efficient segmentation.

Findings

Effective segmentation of artificial and real medical images.

Fast computational method demonstrated through numerical experiments.

Handles both open and closed contours in image analysis.

Abstract

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.