Active contours driven by local Gaussian distribution
Updated 23 Apr 2020
This Matlab code implements a segmentation method using local Gaussian distribution fitting energy, proposed by Li Wang et al's in the paper "Active Contours Driven by Local Gaussian Distribution Fitting Energy. Signal Processing, 89(12), 2009, p. 2435-2447" http://www.sciencedirect.com/science/article/pii/S0165168409000942
The local image intensities are described by Gaussian distributions with different means and variances. The energy minimization is achieved by an interleaved level set evolution and estimation of local intensity means and variances in an iterative process. The means and variances of local intensities are considered as spatially varying functions to handle intensity inhomogeneities and noise of spatially varying strength.
More source codes on image segmentation, such as infant or neonatal brain MR image segmentation using patch-based sparse representation and random forest with auto-context model, can be found in other published papers in the following website:
The code of patch-based sparse representation can be downloaded from here
li wang (2023). Active contours driven by local Gaussian distribution (https://www.mathworks.com/matlabcentral/fileexchange/38637-active-contours-driven-by-local-gaussian-distribution), MATLAB Central File Exchange. Retrieved .
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Patch-based sparse segmentation code is released in my homepage: http://www.unc.edu/~liwa/
add a link of neonatal brain MR segmentation software