Difference Field Estimation for Enhanced 3-D Texture Segmentation

The optimization problem of finding the best match for a thin-plate block of multi-texture 3-D data in a supervised framework is studied in this paper. The textures are modelled as realizations of Gaussian Markov Random Fields (GMRFs)on 3-D lattices. The classification of the central point of the data block is performed by calculating the class probability mass function (p.m.f.s) for the block given the different texture models. The KullbackLeibler measure is proposed for the minimization of the difference between the p.m.f.s distances of the The three-dimensional (3-D) segmentation of volumetric imagery poses the challenge of estimation and compensation for the existing inter-slice difference within a multi-texture 3-D data. In this paper we propose a novel method to identify the difference field by Kullback-Leibler minimization of the distance between the class probability mass functions (p.m.f.s), calculated at thin-plate 3-D blocks of data, centered at the points of interest. and fast FFT-based technique is presented for calculation of the probability density function (p.d.f.) of the data given the model. This facilitates the calculation of the classification p.m.f.s. in a supervised framework. The estimated difference field is used to enhance the performance of a computational-volume based 3-D GMRF segmentation algorithm. The performance of the overall method is illustrated with a simulation study of mosaic of synthetic 3-D textures and MRI images of human brain.