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 thesis is 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 thesis is 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. Two deformable models are developed for the purpose. An explicit shape and region prior deformable model is derived from principal component analysis (PCA) of the contour landmarks obtained from the training images and PCA of the probability distribution inside the prostate region. Moreover, an implicit deformable model is derived from PCA of the signed distance representation of the labeled training data and curve evolution is guided by energy minimization framework of Mumford-Shah (MS) functional. Region based energy is determined from region based statistics of the posterior probabilities. Graph cut energy minimization framework is adopted for prostate segmentation in MRI. Posterior probabilities obtained in a supervised learning schema and from a probabilistic segmentation of the prostate using an at-las are fused in logarithmic domain to reduce segmentation error. Finally a graph cut energy minimization in the stochastic framework achieves prostate segmenta-tion in MRI. Statistically significant improvement in segmentation accuracies are achieved compared to some of the works in literature. Stochastic representation of the prostate region and use of the probabilities in optimization significantly improve segmentation accuracies.

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 thesis is to develop computationally efficient prostate segmentation algorithms in both TRUS and MRI image modalities. In this thesis we propose a probabilistic learning approach to achieve a soft classification of the prostate for automatic initialization and evolution of a deformable model for prostate segmentation. Two deformable models are developed for the TRUS segmentation. An explicit shape and region prior based deformable model and an implicit deformable model guided by an energy minimization framework. Besides, in MRI, the posterior probabilities are fused with the soft segmentation coming from an atlas segmentation and a graph cut based energy minimization achieves the final segmentation. In both image modalities, statistically significant improvement are achieved compared to current works in the literature. / La segmentació de la pròstata en imatge d'ultrasò (US) i de ressonància magnètica (MRI) permet l'estimació del volum, el registre multi-modal i la planificació quirúrgica de biòpsies guiades per imatge. L'objectiu d'aquesta tesi és el desenvolupament d'algorismes automàtics per a la segmentació de la pròstata en aquestes modalitats. Es proposa un aprenentatge automàtic inical per obtenir una primera classificació de la pròstata que permet, a continuació, la inicialització i evolució de diferents models deformables. Per imatges d'US, es proposen un model explícit basat en forma i informació regional i un model implícit basat en la minimització d'una funció d'energia. En MRI, les probalitats inicials es fusionen amb una imatge de probabilitat provinent d'una segmentació basada en atlas, i la minimització es realitza mitjançant tècniques de grafs. El resultat final és una significant millora dels algorismes actuals en ambdues modalitats d'imatge.