| Summary: | Segmentation of the brain into sub-volumes has many clinical applications. Manyneurological diseases are connected with brain atrophy (tissue loss). By dividingthe brain into smaller compartments, volume comparison between the compartmentscan be made, as well as monitoring local volume changes over time. Theformer is especially interesting for the left and right cerebral hemispheres, dueto their symmetric appearance. By using automatic segmentation, the time consumingstep of manually labelling the brain is removed, allowing for larger scaleresearch.In this thesis, three automatic methods for segmenting the brain from magneticresonance (MR) images are implemented and evaluated. Since neither ofthe evaluated methods resulted in sufficiently good segmentations to be clinicallyrelevant, a novel segmentation method, called SB-GC (shape bottleneck detectionincorporated in graph cuts), is also presented. SB-GC utilizes quantitative MRIdata as input data, together with shape bottleneck detection and graph cuts tosegment the brain into the left and right cerebral hemispheres, the cerebellumand the brain stem. SB-GC shows promises of highly accurate and repeatable resultsfor both healthy, adult brains and more challenging cases such as childrenand brains containing pathologies.
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