A Sobolev norm based distance measure for HARDI clustering: a feasibility study on phantom and real data.
Dissimilarity measures for DTI clustering are abundant. However, for HARDI, the L2 norm has up to now been one of only few practically feasible measures. In this paper we propose a new measure, that not only compares the amplitude of diffusion profiles, but also rewards coincidence of the extrema. We tested this on phantom and real brain data. In both cases, our measure significantly outperformed the L2 norm.
Brunenberg
Ellen
E
Biomedical Engineering, Eindhoven University of Technology. e.j.l.brunenberg@tue.nl
Duits
Remco
R
Romeny
Bart ter Haar
Bt
Platel
Bram
B
D000465:Algorithms; D001921:Brain / Q000166:cytology*; D038524:Diffusion Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009474:Neurons / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity