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The three-dimensional probabilistic neural network (PNN) is proposed as the core classifier for segmentation of three-dimensional (3-D) magnetic resonance imaging (MRI). The proposed algorithm takes into account the spatial information between image voxels. It adopts the self-organizing map (SOM) neural network to overly segment the 3D MR image, and yield reference voxels necessary for probabilistic density function. The experimental results demonstrate the effectiveness and robustness of the proposed approach.