1. A method for segmenting a prostate in Magnetic Resonance Image (MRI) data, comprising: a processor learning a boosting classifier from a plurality of Magnetic Resonance (MR) images; the processor determining a center of the prostate by applying the boosting classifier; the processor applying an intensity correction to suppress an image artifact; the processor determining a foreground seed and a background seed of the prostate based on a shape model of the prostate; the processor applying a Random Walker segmentation method to the image data based on the foreground and background seed to generate a segmentation of the prostate; and determining a probability threshold for the Random Walker segmentation method that maximizes a capture of a peripheral zone (PZ) in the segmentation of the prostate.