Cerebral Vascular Tree Matching of 3D-RA Data Based on Tree Edit Distance
作者： W. H. Tang , Albert C. S. Chung
DOI: 10.1007/11812715_15
关键词: Search tree 、 Artificial intelligence 、 Jaro–Winkler distance 、 Tree (data structure) 、 Interval tree 、 Mathematics 、 Tree traversal 、 Edit distance 、 Segment tree 、 Pattern recognition 、 Wagner–Fischer algorithm
摘要: In this paper, we present a novel approach to matching cerebral vascular trees obtained from 3D-RA data-sets based on minimization of tree edit distance. Our is fully automatic which requires zero human intervention. Tree distance term used in the field theoretical computer science describe similarity between two labeled trees. our approach, abstract geometry and morphology vessel branches into labels nodes then use combinatorial optimization strategies compute approximated Once optimal computed, spatial correspondences vessels can be established. By visual inspection experimental results, find that accurate.
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