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Parcellating Whole Brain for Individuals by Simple Linear Iterative Clustering

  • Jing Wang
  • Zilan Hu
  • Haixian WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

This paper utilizes a supervoxel method called simple linear iterative clustering (SLIC) to parcellate whole brain into functional subunits using resting-state fMRI data. The parcellation algorithm is directly applied on the resting-state fMRI time series without feature extraction, and the parcellation is conducted on the individual subject level. In order to obtain parcellations with multiple granularities, we vary the cluster number in a wide range. To demonstrate the reasonability of the proposed approach, we compare it with a state-of-the-art whole brain parcellation approach, i.e., the normalized cuts (Ncut) approach. The experimental results show that the proposed approach achieves satisfying performances in terms of spatial contiguity, functional homogeneity and reproducibility. The proposed approach could be used to generate individualized brain atlases for applications such as personalized medicine.

Keywords

Whole brain parcellation Supervoxel Resting-state fMRI Functional connectivity Individualized brain atlas 

Notes

Acknowledgements

This work was supported in part by the National Basic Research Program of China under Grant 2015CB351704, the National Natural Science Foundation of China under Grant 61375118, and the Research Foundation for Young Teachers in Anhui University of Technology under Grant QZ201516.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Key Lab of Child Development and Learning Science of Ministry of Education, Research Center for Learning ScienceSoutheast UniversityNanjingChina
  2. 2.School of Mathematics and PhysicsAnhui University of TechnologyMaanshanChina

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