Disease classification with hippocampal shape invariants.
We present an Alzheimer's detection study based on a global shape description of hippocampal surface models. With global descriptors forming our bag of features, Support Vector Machine classification of 49 Alzheimer (AD) and 63 elderly control subjects yielded 75.5% sensitivity and 87.3% specificity with 82.1% correct overall in a leave-one-out test. We show that our description contributes new information to simpler shape measures. Armed with a rigid shape registration tool, we also present a way to visualize variation in global shape description as a local displacement map, thus clarifying the descriptors' anatomical meaning.
Laboratory of Neuro Imaging, Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, California, USA. bgutman@ucla.edu
Boris
B
Gutman
Yalin
Y
Wang
Jonathan
J
Morra
Arthur W
AW
Toga
Paul M
PM
Thompson
D000368:Aged; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000473:pathology* / Q000523:psychology; D016208:Databases, Factual; D005260:Female; D006624:Hippocampus / Q000473:pathology*; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D008297:Male; D008953:Models, Anatomic; D009483:Neuropsychological Tests; D009929:Organ Size; D012372:ROC Curve; D012044:Regression Analysis; D012680:Sensitivity and Specificity