BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classification

MOĞULTAY ÖZCAN H., YARMAN VURAL F. T.

2018 International Workshop on Graphs in Biomedical Image Analysis-GRAIL, Granada, Nikaragua, 20 Eylül 2018, cilt.11044, ss.32-42, (Tam Metin Bildiri) identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 11044
  • Doi Numarası: 10.1007/978-3-030-00689-1_4
  • Basıldığı Şehir: Granada
  • Basıldığı Ülke: Nikaragua
  • Sayfa Sayıları: ss.32-42
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.