Non-homogenous image segmentation with global optimization

In order to solve the problem that the traditional geometric active contour model can not adaptively segment a non-homogenous image, a global optimization non-homogenous image segmentation algorithm is proposed. Firstly, a new energy function is defined by importing gradient information on the inhomogeneous image which is filtered by the Gaussian filter. Then, the domain of the energy function is extended by the level set method. Thus, the energy function has the solution of global optimization. We introduce a level set function control term for avoiding the re-initialization procedure of the level set function. Finally, a partial difference equation of the level set function evolvement is derived by minimizing the energy function. The non-homogenous image segmentation is implemented by the numerical solution of the partial difference equation. Experimental results show that the proposed algorithm not only can automatically determine the evolvement orientation of the active contour cure, but also can effectively segment non-homogenous images.