基于配准框架的星形分割模型，可处理遮挡与噪声下的精准分割。
A Registration-Based Star-Shape Segmentation Model and Fast Algorithms
- 将水平集与配准框架结合，支持单/多中心星形分割。
- 在合成与真实图像上均实现高精度分割，边界可对齐指定关键点。
- 适合需要几何先验的医学或遥感图像分割任务。
图像分割在提取感兴趣目标及其边界中至关重要。然而，在存在遮挡、模糊或噪声的退化图像中，准确分割面临挑战。为此，本文提出一种基于配准框架的星形分割模型。通过将水平集表示与配准框架结合，并对变形的水平集函数施加约束，该模型能够实现完整与部分星形分割，支持单中心或多中心结构。此外，方法允许已识别的边界强制通过指定的地标位置。我们采用交替方向乘子法求解所提模型。在合成图像与真实图像上的数值实验表明，该方法在实现精确星形分割方面具有显著有效性。
原文摘要 · Abstract (English)
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.
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