Novel statistical and geometric models for automated brain tissue labeling in magnetic resonance images

Analysis of brain tissues such as white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and pathological regions from magnetic resonance imaging (MRI) scans of normal adults and patients with neurodegenerative diseases such as multiple sclerosis (MS) allows for improved understanding of disease progression in vivo. As images are often confounded by acquisition noise and partial-volume effects, developing an automatic, robust, and efficient segmentation is essential to the accurate quantification of disease severity. Existing methods often require subjective parameter tuning, anatomical atlases, and training, which are impractical and undesirable. The contributions of this thesis are three-fold. First, a 3D deformable model was explored by integrating statistical and geometric information into a novel hybrid feature to provide robust regularization of the evolving contours. Second, to improve efficiency and noise resiliency, a 3D region-based hidden Markov model (rbHMM) was developed. The novelty of this model lies in subdividing an image into irregularly-shaped regions to reduce the problem dimensionality. A tree-structured estimation algorithm, based on Viterbi decoding, then enabled rotationally invariant estimation of the underlying discrete tissue labels given noisy observations. Third, estimation of partial volumes was incorporated in a 3D fuzzy rbHMM (frbHMM) for analyzing images suffering from acquisition-related resolution limitation by incorporating forward-backward estimations. These methods were successfully applied to the segmentation of WM, GM, CSF, and white matter lesions. Extensive qualitative and quantitative validations were performed on both synthetic 3D geometric shapes and simulated brain MRIs before applying to clinical scans of normal adults and MS patients. These experiments demonstrated 40% and 10% improvement in segmentation efficiency and accuracy, respectively, over state-of-the-art approaches under noise. When modeling partial-volume effects, an additional 30% reduction in segmentation errors was observed. Furthermore, the rotational invariance property introduced is especially valuable as segmentation should be invariant to subject positioning in the scanner to minimize analysis variability. Given such improvement in the quantification of tissue volumes, these methods could potentially be extended to the studies of other neurodegenerative diseases such as Alzheimer’s. Furthermore, the methods developed in this thesis are general and can potentially be adopted in other computer vision-related segmentation applications in the future.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat