A new 3D automatic segmentation framework for accurate extraction of prostate from diffusion imaging

Abstract

Prostate segmentation is an essential step in developing any non-invasive 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 Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum the Posteriori (MAP) estimate of a new log-likelihood function that consists of three descriptors: (i) 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> -order visual appearance descriptors of the Diffusion-MRI, (ii) a 3D spatially rotation-variant 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> -order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate Diffusion-MRI 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> -order rotation-variant Markov-Gibbs Random Field (MGRF) of object/background labels with analytically estimated potentials. Experiments with real in vivo prostate Diffusion-MRI confirm the robustness and accuracy of the proposed approach.