| The investigation of genetic changes on the morphological phenotype of small animal models has become a vital part in understanding the etiology and pathogenesis of neurodegenerative disorders. Morphometric analysis of structure volume, shape and spatial organization in the brain has become a critical indicator of pathology and disease. Magnetic resonance imaging is an attractive modality for this purpose due to its non-invasiveness, inherently 3D digital nature and excellent soft tissue contrast. The aim of this work was to develop an automated segmentation strategy for the complete labeling of the MR volume of the mouse brain into constituent structures.; Although the strength of magnetic resonance imaging in the assessment of human brain structure and function is well established, its translation to the arena of small animal imaging is not trivial. Advancement to high field imaging, small compact coils and technically intensive gradient systems has contributed significantly in enabling high resolution imaging of the mouse. However, radical differences in the MR relaxation parameters for mouse brain tissue at high fields and its resultant affects on the achievable signal and contrast has made structure definition and automated labeling a difficult challenge.; The thesis provides solutions for both, the MR acquisition to obtain maximum differential contrast between tissues and the subsequent segmentation of the acquired data into different structures. The acquisitions were optimized for obtaining high contrast definition between structures at reasonable scan times. We integrate the MR intensity information with spatial priors to arrive at a classification of brain tissue into individual structures. Results are provided for the formalin fixed brain at 90 micron isotropic resolution and the actively stained (perfusion with a contrast agent) mouse brain at isotropic 20 micron resolution. Results indicate a high accuracy in the automated labeling of the mouse brain. This work is a significant step in the morphological phenotyping of mouse models. |
