DocumentCode :
1813523
Title :
Automatic extraction and matching of neonatal cerebral vasculature
Author :
Xue, Hui ; Malamateniou, Christina ; Allsop, Joanna ; Srinivasan, Latha ; Hajnal, Jo V. ; Rueckert, Daniel
Author_Institution :
Dept. of Imaging Sci., Hammersmith Hosp., London
fYear :
2006
fDate :
6-9 April 2006
Firstpage :
125
Lastpage :
128
Abstract :
This paper presents a method for extracting and matching the neonatal cerebral vasculature from magnetic resonance angiography-time of flight (MRA-TOF) images. The vasculature is first extracted using a fully automatic version of the ridge traversal algorithm. Extracted vessel segments are then connected iteratively to compose a vessel tree that is automatically labeled. After this, an indirect vasculature registration method is used to recover global deformation between two vessel trees and vessel matching is performed by comparing a cost function measuring average spatial distance between two vessel branches. This process starts from roots of the trees and continues until the leaf branches are reached. The robustness and accuracy of vessel matching are improved by performing subtree registration using a robust bootstrap extension of the iterative closest point (ICP) algorithm. Experiments on data from neonatal brains show the effectiveness of the proposed vessel extraction and matching methods for analysis arteries subject to change resulting from growth and development
Keywords :
biomedical MRI; blood vessels; brain; feature extraction; image matching; image registration; iterative methods; medical image processing; paediatrics; MRA-TOF images; arteries; automatic neonatal cerebral vasculature extraction; cost function; extracted vessel segments; global deformation recovery; indirect vasculature registration method; iterative closest point algorithm; iterative methods; magnetic resonance angiography-time of flight images; neonatal brains; neonatal cerebral vasculature matching; ridge traversal algorithm; subtree registration; vessel tree; Arteries; Cost function; Data mining; Image segmentation; Iterative algorithms; Iterative closest point algorithm; Magnetic resonance; Pediatrics; Performance evaluation; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-9576-X
Type :
conf
DOI :
10.1109/ISBI.2006.1624868
Filename :
1624868
Link To Document :
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