A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI

Prostate segmentation is an essential step in developing any noninvasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement MRI (DCE-MRI) is proposed. The framework is based on Maximum A Posteriori (MAP) estimate of a new log-likelihood function that consists of : (i) 1^st-order visual appearance descriptors of the DCE-MRI, (ii) a 3D spatially rotation-variant 2^nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate data. The spatial interactions between the prostate voxels are modeled by a 3D 2^nd-order rotation-variant Markov-Gibbs Random Field (MGRF) of object/background labels with analytically estimated potentials. Experiments with in vivo prostate DCE-MRI confirm the robustness and accuracy of the proposed approach.