Robust Image Segmentation Applied to Magnetic Resonance and Ultrasound Images of the Prostate Soumya

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 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. 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. iii te l-0 08 37 72 2, v er si on 1 24 J un 2 01 3