Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonanceimages (MRI) facilitates volume estimation, multi-modal image registration, surgicalplaning and image guided prostate biopsies. The objective of this thesis is to developshape and region prior deformable models for accurate, robust and computationallyefficient prostate segmentation in TRUS and MRI images. Primary contributionof this thesis is in adopting a probabilistic learning approach to achieve soft classificationof the prostate for automatic initialization and evolution of a shape andregion prior deformable models for prostate segmentation in TRUS images. Twodeformable models are developed for the purpose. An explicit shape and regionprior deformable model is derived from principal component analysis (PCA) of thecontour landmarks obtained from the training images and PCA of the probabilitydistribution inside the prostate region. Moreover, an implicit deformable model isderived from PCA of the signed distance representation of the labeled training dataand curve evolution is guided by energy minimization framework of Mumford-Shah(MS) functional. Region based energy is determined from region based statistics ofthe posterior probabilities. Graph cut energy minimization framework is adoptedfor prostate segmentation in MRI. Posterior probabilities obtained in a supervisedlearning schema and from a probabilistic segmentation of the prostate using an atlasare fused in logarithmic domain to reduce segmentation error. Finally a graphcut energy minimization in the stochastic framework achieves prostate segmentationin MRI. Statistically significant improvement in segmentation accuracies areachieved compared to some of the works in literature. Stochastic representation ofthe prostate region and use of the probabilities in optimization significantly improvesegmentation accuracies
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00837722 |
| Date | 19 October 2012 |
| Creators | Ghose, Soumya |
| Publisher | Université de Bourgogne |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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