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BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classification

  • Hazal Mogultay
  • Fatos Tunay Yarman Vural
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11044)

Abstract

In this study, we propose a novel brain parcellation algorithm, called BrainParcel. BrainParcel defines a set of supervoxels by partitioning a voxel level brain graph into a number of subgraphs, which are assumed to represent “homogeneous” brain regions with respect to a predefined criteria. Aforementioned brain graph is constructed by a set of local meshes, called mesh networks. Then, the supervoxels are obtained using a graph partitioning algorithm. The supervoxels form partitions of brain as an alternative to anatomical regions (AAL). Compared to AAL, supervoxels gather functionally and spatially close voxels. This study shows that BrainParcel can achieve higher accuracies in cognitive state classification compared to AAL. It has a better representation power compared to similar brain segmentation methods, reported the literature.

Keywords

fMRI Brain partitioning Mesh model 

Notes

Acknowledgement

This project is supported by TUBITAK under grant number 116E091. We thank UMRAM Center of Bilkent University for opening their facilities to collect fMRI dataset. We also thank to Dr. Itir Onal Ertugrul and Dr. Orhan Firat for their contribution and effort of data collection.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey

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