MBO Scheme for Local Chan-Vese Segmentation
Kevin Bui, Adina Ciomaga
⚠ This is a preprint. It may change before it is accepted for publication.

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.

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