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Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonance images (MRI) facilitates volume estimation, multi-modal image registration, surgical planing and image guided prostate biopsies. The objective of this work was to develop shape and region prior deformable models for accurate, robust and computationally efficient prostate segmentation in TRUS and MRI images. Primary contribution of this work was in adopting a probabilistic learning approach to achieve soft classification of the prostate for automatic initialization and evolution of a shape and region prior deformable models for prostate segmentation in TRUS images. Region based energy was determined from region based statistics of the posterior probabilities. Graph cut energy minimization framework was adopted for prostate segmentation in MRI. Posterior probabilities obtained in a supervised learning schema and from a probabilistic segmentation of the prostate using an atlas are fused in logarithmic domain to reduce segmentation error. Finally a graph cut energy minimization in the stochastic framework achieves prostate segmentation in MRI.