Skeletons in N dimensions using shape primitives
Pattern Recognition Letters
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
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Accurate diagnostic assessment of metabolic processes in nuclear medicine diagnostic imaging, e.g. SPECT and PET, rely on specific localization of physiological activity. The major step in precise staging of neuro-degenerative diseases is robust, patient-specific classification of the brain. In this work a vascularization-based classification strategy for MRI datasets of the brain is introduced to handle variability of patient's anatomy. The vascularization-based classification utilizes skeletonization in combination with m-adjacency to construct a hierarchical vessel tree from binary pre-segmentations. Based on the vessel topology, the brain voxels are classified with respect to a minimal distance criterion from the vessel branches they are assigned to. This blood-supply oriented approach shows proper segmentation of respective anatomical regions of the human brain. Results are validated on T1-weighted brainweb database.