Disease classification with hippocampal shape invariants

Hippocampus. 2009 Jun;19(6):572-8. doi: 10.1002/hipo.20627.

Abstract

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.

MeSH terms

  • Aged
  • Alzheimer Disease / classification
  • Alzheimer Disease / diagnosis*
  • Alzheimer Disease / pathology*
  • Alzheimer Disease / psychology
  • Databases, Factual
  • Female
  • Hippocampus / pathology*
  • Humans
  • Imaging, Three-Dimensional
  • Magnetic Resonance Imaging
  • Male
  • Models, Anatomic
  • Neuropsychological Tests
  • Organ Size
  • ROC Curve
  • Regression Analysis
  • Sensitivity and Specificity