用MBO算法高效求解局部Chan-Vese分割模型
MBO Scheme for Local Chan--Vese Segmentation
- 基于MBO框架设计新算法，替代传统有限差分法
- 支持双相与多相分割，可处理灰度与彩色图像
- 在医学和显微图像上表现稳定，适合实际应用
针对强度不均匀图像，局部Chan-Vese（LCV）模型通过引入每个像素周围的局部统计信息，扩展了经典Chan-Vese（CV）分割方法。最初，LCV模型采用有限差分法求解，沿用CV模型的策略。作为有限差分法的替代方案，一种基于Merriman-Bence-Osher（MBO）框架的更高效算法曾被用于求解CV模型。本文推导出类似MBO算法以求解LCV模型，并提出一种高效的实现方式。该算法适用于双相与多相分割，同时讨论了向彩色图像的扩展。为验证所提方法的有效性，我们在多种灰度与彩色图像上进行了实验，包括医学图像和显微图像。
原文摘要 · Abstract (English)
Robust to intensity inhomogeneity, the local Chan--Vese (LCV) model extends the classical Chan--Vese (CV) image segmentation method by incorporating local statistical information around each pixel. Originally, the LCV model was solved using a finite difference scheme, following the approach used for the CV model. As an alternative to the finite difference scheme, a more efficient algorithm based on the Merriman-Bence-Osher (MBO) scheme was later developed for the CV model. In this paper, we derive a similar MBO-based algorithm to solve the LCV model and propose an efficient implementation. The algorithm is developed for both two-phase and multiphase segmentation, and an extension to color images is also discussed. To demonstrate the effectiveness of the proposed approach, we apply it to a variety of grayscale and color images, including medical and microscopy images.
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