title: NEURONAL WHITE MATTER PARCELLATION USING SPATIALLY COHERENT NORMALIZED CUTS.
abstract: This work presents an automated method for partitioning neuronal white matter (WM) into regions of interest with uniform WM architecture. These regions can then be used to replace atlas-derived regions for any subsequent statistical analysis. The fiber orientation distribution function is used as a model of WM architecture resulting in a voxel similarity function sensitive to both fiber orientations and configurations. The method utilizes the normalized cuts algorithm to partition WM voxels based on this similarity function along with a connected component labeling algorithm to ensure spatial compactness. We illustrate the algorithms ability to discern regions based on both orientation and complexity through its application to a simulated fiber crossing and an in-vivo dataset.
authors.affiliation: Department of Bioengineering, University of Pennsylvania.
authors.forename: Luke
authors.initials: L
authors.lastname: Bloy
authors.forename: Madhura
authors.initials: M
authors.lastname: Ingalhalikar
authors.forename: Ragini
authors.initials: R
authors.lastname: Verma