🤖 AI Summary
This work addresses the limited robustness of the local Chan–Vese model to intensity inhomogeneity and the computational inefficiency of conventional finite difference schemes by introducing, for the first time, the Merriman–Bence–Osher (MBO) scheme into this framework. The proposed method formulates an efficient variational level set approach grounded in local image statistics, significantly accelerating computation while naturally accommodating two-phase, multi-phase, and color image segmentation. Extensive experiments on diverse datasets—including medical and microscopic images—demonstrate that the algorithm achieves superior segmentation accuracy and speed compared to traditional finite difference methods, all while maintaining high robustness to intensity variations.
📝 Abstract
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.