DocumentCode :
2720867
Title :
Automatic cortical surface parcellation based on fiber density information
Author :
Zhang, Degang ; Guo, Lei ; Li, Gang ; Nie, Jingxin ; Fan Deng ; Li, Kaiming ; Hu, Xintao ; Zhang, Tuo ; Jiang, Xi ; Zhu, Dajiang ; Zhao, Qun ; Liu, Tianming
Author_Institution :
Sch. of Autom., Northwestern Polytech. Univ., Xi´´an, China
fYear :
2010
fDate :
14-17 April 2010
Firstpage :
1133
Lastpage :
1136
Abstract :
It is widely believed that the structural connectivity of a brain region largely determines its function. High resolution Diffusion Tensor Imaging (DTI) is now able to image the axonal fibers in vivo and the DTI tractography result provides rich connectivity information. In this paper, a novel method is proposed to employ fiber density information for automatic cortical parcellation based on the premise that fibers connecting to the same cortical region should be within the same functional brain network and their aggregation on the cortex can define a functionally coherent region. This method consists of three steps. Firstly, the fiber density is calculated on the cortical surface. Secondly, a flow field is obtained by calculating the fiber density gradient and a flow field tracking method is utilized for cortical parcellation. Finally, an atlas-based warping method is used to label the parcellated regions. Our method was applied to parcellate and label the cortical surfaces of eight healthy brain DTI images, and interesting results are obtained. In addition, the labeled regions are used as ROIs to construct structural networks for different brains, and the graph properties of these networks are measured.
Keywords :
biomedical MRI; brain; neurophysiology; DTI tractography; atlas-based warping method; automatic cortical parcellation; automatic cortical surface parcellation; axonal fibers; brain region; connectivity information; fiber density information; functional brain network; graph properties; high resolution diffusion tensor imaging; structural connectivity; Automation; Computer science; Diffusion tensor imaging; Flowcharts; Image reconstruction; Image resolution; In vivo; Joining processes; Physics; Surface reconstruction; Cortical surface parcellation; fiber density; structure network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on
Conference_Location :
Rotterdam
ISSN :
1945-7928
Print_ISBN :
978-1-4244-4125-9
Electronic_ISBN :
1945-7928
Type :
conf
DOI :
10.1109/ISBI.2010.5490193
Filename :
5490193
Link To Document :
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