Nonparametric MDL segmentation of inhomogeneous images based on Quadratic Local Binary Fitting

This paper addresses the problem of two-region noisy image segmentation in the presence of intensity inhomogeneity and of unknown noise fluctuations. For that purpose, the inhomogeneity is modeled as spatial variations of the mean intensity (which are different inside and outside the object) and are estimated using Local Binary Fitting (LBF) approach. In order to be robust to non standard noise phenomena, the intensity fluctuations are then modeled with nonparametric probability density functions (pdf) leading to a new polygonal active contour segmentation technique based on a Minimum Description Length (MDL) criterion which does not require a priori knowledge on the intensity fluctuations and on the inhomogeneity present in the image. Furthermore, it will be shown that in the case of highly inhomogeneous images, the standard LBF approach used to estimate the intensity inhomogeneity can be generalized to Quadratic Local Binary Fitting (QLBF) in order to improve the performance of the proposed segmentation technique.