A slicing-based coherence measure for clusters of DTI integral curves.
We present a slicing-based coherence measure for clusters of DTI integral curves. For a given cluster, we probe samples from the cluster by slicing it with a plane at regularly spaced locations parametrized by curve arc lengths. Then we compute a stability measure based on the spatial relations between the projections of the curve points in individual slices and their change across the slices. We demonstrate its use in refining agglomerative hierarchical clustering results of DTI curves that correspond to neural pathways. Expert evaluation shows that refinement based on our measure can lead to improvement of clustering that is not possible directly by using standard methods.
Demiralp
Cağatay
C
Brown University, USA.
Shakhnarovich
Gregory
G
Zhang
Song
S
Laidlaw
David H
DH
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity