  A novel locally statistical active contour model (ACM) for image segmentation
in the presence of intensity inhomogeneity is presented in this paper. The
inhomogeneous objects are modeled as Gaussian distributions of different means
and variances, and a moving window is used to map the original image into
another domain, where the intensity distributions of inhomogeneous objects are
still Gaussian but are better separated. The means of the Gaussian
distributions in the transformed domain can be adaptively estimated by
multiplying a bias field with the original signal within the window. A
statistical energy functional is then defined for each local region, which
combines the bias field, the level set function, and the constant approximating
the true signal of the corresponding object. Experiments on both synthetic and
real images demonstrate the superiority of our proposed algorithm to
state-of-the-art and representative methods.
