This Matlab/C code contains routines to perform level set image segmentation according to:
(1) various multiphase (multiregion) formulations, including a fast scheme where the computation load grows linearly with the number of regions and,
(2) various region-based image descriptions which generalize the standard piecewise constant Chan-Vese model; the descriptions include Gamma distribution models for image data corrupted by multiplicative noise as in remote sensing synthetic aperture radar (SAR), and medical imaging ultrasound. Also included is kernel mapping as an alternative to explicit image modeling.
Complete details on usage and compilation can be found in the enclosed pdf file (Readme.pdf). The functions were tested on the following versions of MATLAB and C.
MATLAB version: 18.104.22.1681 (R2008b)
C compiler: Lcc-win32 C 2.4.1
The code implements the level set methods in the following papers (the papers are included in the package):
I. Ben Ayed, A. Mitiche, and Z. Belhadj, “Multiregion level set partitioning on synthetic aperture radar images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 793–800, 2005.
M. Ben Salah, A. Mitiche and I. Ben Ayed, “Effective Level Set Image Segmentation with a Kernel Induced Data Term,” IEEE Transactions on Image processing, vol. 19, no 1, pp. 220-232, 2010.
I. Ben Ayed, A. Mitiche, and Z. Belhadj, “Polarimetric image segmentation via maximum likelihood approximation and efficient multiphase level sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1493–1500, 2006.
I. Ben Ayed, A. Mitiche, “A Partition Constrained Minimization Scheme for Efficient Multiphase Level Set Image Segmentation,” IEEE ICIP 2006, pp. 1641-1644.
I. Ben Ayed and A. Mitiche, “A region merging prior for variational level set image segmentation,” IEEE Transactions on Image Processing, vol. 17, no. 12, pp. 2301–2313, 2008.
A. Mansouri, A. Mitiche, and C. Vazquez, “Multiregion competition: A level set extension of region competition to multiple region partioning,” Computer Vision and Image Understanding, vol. 101, no. 3, pp. 137–150, 2006.
C. Vazquez, A. Mitiche, Ismail Ben Ayed, “Image segmentation as regularized clustering: a fully global curve evolution method,” IEEE ICIP 2004, pp. 3467-3470.
Formal and complete details on the implementations as well as on the derivation of the level set evolution equations from various energy functional types can be found in the book “Variational and Level Set Methods in Image Segmentation, by A. Mitiche and I. Ben Ayed, 2010, Springer, 1st edition”