A Fast and Robust Level Set Method for Image Segmentation Using Fuzzy Clustering and LBM

Here is the implementation of our paper IEEE TSMCB. 2012.2218233

In the last decades, due to the development of the parallel programming, the lattice Boltzmann method (LBM) has attracted much attention as a fast alternative approach for solving partial differential equations. In this paper, we first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity
inhomogeneity of the real-world image. Using the gradient descent method, we obtained the corresponding level set equation from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is fast, robust against noise, independent to the position of the initial contour, effective in the presence of intensity inhomogeneity, highly parallelizable and can detect objects with or without edges. Experiments on medical and
real-world images demonstrate the performance of the proposed method in terms of speed and efficiency.
The present Matlab codes implements this paper. You proceed as follows:

1- Open the main code,
2- Put the adress of the image you want to segment,
3- Run the code (optional : add to path or change the folder)
4- Select the initial contour using the mouse,
5- And finally double click on the initial contour.

If you use any of these codes, please cite the implemented paper as follow

S. Balla-Arabé, X. Gao and B. Wang, “A Fast and Robust Level Set Method for Image Segmentation Using Fuzzy Clustering and Lattice Boltzmann Method”, IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol. 43, no. 3, pp. 910-920, 2013.

If you have any question please let me know.

E -mail: [e-mail]
Google Scholar link: http://scholar.google.fr/citations?user=dYEIx_IAAAAJ&hl=fr&oi=sra