Probabilistic modeling for segmentation in magnetic resonance images of the human brain

This thesis deals with the fully automatic generation of semantic annotations for medical imaging data by means of medical image segmentation and labeling. In particular, we focus on the segmentation of the human brain and related structures from magnetic resonance imaging (MRI) data. We present three novel probabilistic methods from the field of database-guided knowledge-based medical image segmentation. We apply each of our methods to one of three MRI segmentation scenarios: 1) 3-D MRI brain tissue classification and intensity non-uniformity correction, 2) pediatric brain cancer segmentation in multi-spectral 3-D MRI, and 3) 3-D MRI anatomical brain structure segmentation. All the newly developed methods make use of domain knowledge encoded by probabilistic boosting-trees (PBT), which is a recent machine learning technique. For all the methods we present uniform probabilistic formalisms that group the methods into the broader context of probabilistic modeling for the purpose of image segmentation. We show by comparison with other methods from the literature that in all the scenarios our newly developed algorithms in most cases give more accurate results and have a lower computational cost. Evaluation on publicly available benchmarking data sets ensures reliable comparability of our results to those of other current and future methods. We also document the participation of one of our methods in the ongoing online caudate segmentation challenge (www.cause07.org), where we rank among the top five methods for this particular segmentation scenario.