Efficient 3D volume segmentation of MR images by a modified deterministic annealing approach

This paper presents the results of applying the deterministic annealing (DA) algorithm to simulated magnetic resonance image segmentation. The applicability of this methodology for 3-D segmentation has been rigorously tested by using the simulated MRI volumes of normal brain at the Brain Web [8] for the 181 slices and whole volume of different modalities (T1, T2, and PD) without and with various levels of noise and intensity inhomogeneities. With proper thresholding of the clusters formed by the modified DA almost zero misclassification was achieved without the presence of noise. Even up to 7% addition of noise and 40% inhomogeneity, the average misclassification rates of the voxels belonging to white matter, gray matter, and cerebrospinal fluid were found to be less than 5% after median filtering. The accuracy, stability, global optimization and speed of the DA algorithm for 3-D MR image segmentation could provide a more rigorous tool for identification of diseased brain tissues from 3-D MR images than other existing 3-D segmentation techniques. Further inquiry into the DA algorithm shows that it is a Bayesian classifier with the assumption that the data to be classified follow a multivariate normal distribution. The characteristic of being a Bayesian classifier guarantees its achievement of global optimization.