A Registration-Based Star-Shape Segmentation Model and Fast Algorithms

TL;DR

This paper introduces a novel registration-based star-shape segmentation model that effectively handles occlusions and noise, utilizing level set representation and landmark constraints for improved accuracy in synthetic and real images.

Contribution

It presents a new star-shape segmentation framework combining registration, level set methods, and landmark constraints, capable of full and partial segmentation with multiple centers.

Findings

Accurate segmentation demonstrated on synthetic images.

Effective handling of occlusions and noise.

Robust performance on real images.

Abstract

Image segmentation plays a crucial role in extracting objects of interest and identifying their boundaries within an image. However, accurate segmentation becomes challenging when dealing with occlusions, obscurities, or noise in corrupted images. To tackle this challenge, prior information is often utilized, with recent attention on star-shape priors. In this paper, we propose a star-shape segmentation model based on the registration framework. By combining the level set representation with the registration framework and imposing constraints on the deformed level set function, our model enables both full and partial star-shape segmentation, accommodating single or multiple centers. Additionally, our approach allows for the enforcement of identified boundaries to pass through specified landmark locations. We tackle the proposed models using the alternating direction method of multipliers. Through numerical experiments conducted on synthetic and real images, we demonstrate the efficacy of our approach in achieving accurate star-shape segmentation.