Abstract #4174
Parcellating Brain Cortical Regions at Multiple Levels of Granularity using the Weighted K-means Algorithm
Shih-Yen Lin 1,2 , Hengtai Jan 1 , Tsang-Chu Yu 3 , Yi-Ping Chao 3 , Kuan-Hung Cho 4 , and Li-Wei Kuo 1
1
Institute of Biomedical Engineering and
Nanomedicine, National Health Research Institutes,
Miaoli, Taiwan,
2
Department
of Computer Science, National Chiao Tung University,
Hsinchu, Taiwan,
3
Department
of Computer Science and Information Engineering, Chang
Gung University, Taoyuan, Taiwan,
4
Institute
of Brain Science, National Yang-Ming University, Taipei,
Taiwan
To investigate the brain networks at multiple scales,
recent studies have attempted to divide the cortical
regions into smaller parcels at multiple levels of
granularity. In this study, we proposed a parcellation
method based on the weighted k-means algorithm with the
following desirable features, including similar
subdivision volume size over the whole brain, not
fragmented, fully deterministic and highly reproducible.
A quantitative evaluation with calculating the
coefficient of variance among all parcels was performed.
Our results show the variances significantly drop
between intermediate to finest levels, suggesting that
the clustering sizes become more uniformly. Future works
include developing more quantitative evaluation
parameters, demonstration on other brain atlases and
application on brain network analysis at multiple
scales.
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