A Registration-Based Star-Shape Segmentation Model and Fast Algorithms
This work addresses segmentation problems in computer vision for applications like medical imaging, but it is incremental as it builds on existing star-shape priors and registration frameworks.
The paper tackled the challenge of accurate image segmentation under occlusions or noise by proposing a registration-based star-shape segmentation model that enables full or partial segmentation with single or multiple centers, and demonstrated its efficacy in achieving accurate results through numerical experiments on synthetic and real images.
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.