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A novel non-invasive approach for the early diagnosis of prostate cancer from diffusion-weighted MRI is proposed. The proposed diagnostic approach consists of three main steps. The first step is to isolate the prostate from the surrounding anatomical structures based on a Maximum a Posteriori (MAP) estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of prostate tissues and its background (surrounding anatomical structures). In the second step, a nonrigid registration algorithm is employed to account for any local deformation between the segmented prostates at different b-values that could occur during the scanning process due to patient breathing and local motion. In the final step, a kn-Nearest Neighbor-based classifier is used to classify the prostate into benign or malignant based on four appearance features extracted from registered images. Moreover, in this paper we introduce a new approach to generate color maps that illustrate the propagation of diffusion in prostate tissues based on the analysis of the 3D spatial interaction of the change of the gray level values of prostate voxel using a Generalized Gauss-Markov Random Field (GGMRF) image model. Finally, the tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Experimental results on 28 clinical diffusion-weighted MRI data sets yield promising results.