A hybrid approach to automatic clustering of white matter fibers.
Recently, the tract-based white matter (WM) fiber analysis has been recognized as an effective framework to study the diffusion tensor imaging (DTI) data of human brain. This framework can provide biologically meaningful results and facilitate the tract-based comparison across subjects. However, due to the lack of quantitative definition of WM bundle boundaries, the complexity of brain architecture and the variability of WM shapes, clustering WM fibers into anatomically meaningful bundles is nontrivial. In this paper, we propose a hybrid top-down and bottom-up approach for automatic clustering and labeling of WM fibers, which utilizes both brain parcellation results and similarities between WM fibers. Our experimental results show reasonably good performance of this approach in clustering WM fibers into anatomically meaningful bundles.
Li
Hai
H
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Xue
Zhong
Z
Guo
Lei
L
Liu
Tianming
T
Hunter
Jill
J
Wong
Stephen T C
ST
D000465:Algorithms; D001331:Automation; D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D019992:Databases as Topic; D056324:Diffusion Tensor Imaging / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009413:Nerve Fibers, Myelinated; D009434:Neural Pathways / Q000033:anatomy & histology; D009929:Organ Size