A faster-converging algorithm for image segmentation with a modified Chan-Vese model
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Johns Hopkins Univ., Baltimore, MD (United States)
We propose an algorithm for segmentation of grayscale images. Our algorithm computes a solution to the convex, unconstrained minimization problem proposed by T. Chan., S. Esedoglu, and M. Nikolova in [1], which is closely related to the Chan-Vese level set algorithm for the Mumford-Shah segmentation model. Up to now this problem has been solved with a gradient descent method. Our approach is a quasi-Newton method based on the lagged diffusivity algorithm [2] for minimizing the total-variation functional for image denoising [3]. Our results show that our algorithm requires a much smaller number of iterations and less time to converge than gradient descent, and is able to segment noisy images correctly.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1454974
- Report Number(s):
- LA-UR--07-07900
- Country of Publication:
- United States
- Language:
- English
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