发表机构
University of California, Irvine; Université Paris Cité; Sorbonne Université; Romanian Academy（加利福尼亚大学欧文分校; 巴黎城市大学; 索邦大学; 罗马尼亚科学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于MBO方案的算法，用于求解对强度不均匀性鲁棒的局部Chan-Vese模型，适用于二相、多相及彩色图像分割，在医学、显微等图像上验证了有效性。
AI 中文摘要
局部Chan-Vese（LCV）模型对强度不均匀性具有鲁棒性，它通过融合每个像素周围的局部统计信息，扩展了经典的Chan-Vese（CV）图像分割方法。最初，LCV模型采用与CV模型相同的有限差分方案求解。作为有限差分方案的替代方案，后来为CV模型开发了一种基于Merriman-Bence-Osher（MBO）方案的更高效算法。本文中，我们推导了一种类似的基于MBO的算法来求解LCV模型，并提出了一种高效实现。该算法适用于二相和多相分割，还讨论了对彩色图像的扩展。为证明所提方法的有效性，我们将其应用于多种灰度和彩色图像，包括医学图像和显微图像。
英文摘要
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.
CommentsAccepted to Image Processing On Line; Github link to code: https://github.com/kbui1993/Official_LCV_Code
