title: Extraction of sulcal medial surface and classification of Alzheimer's disease using sulcal features.
abstract: BACKGROUND
  Recent advancements in medical imaging have resulted in a significant growth in diagnostic possibilities of neurodegenerative disorders. Neuroanatomical abnormalities of the cerebral cortex in Alzheimer's disease (AD), the most frequent type of dementia in the elderly, can be observed in morphology analysis of cortical sulci, and used to distinguish between cognitively normal (CN) subjects and subjects with AD.
  
  
  OBJECTIVE
  The purpose of this paper was to extract sulcal features by means of computing a sulcal medial surface for AD/CN classification.
  
  
  METHODS
  24 distinct sulci per subject were extracted from 210 subjects from the ADNI database by the BrainVISA sulcal identification pipeline. Sulcal medial surface features (depth, length, mean and Gaussian curvature, surface area) were computed for AD/CN classification with a support vector machine (SVM).
  
  
  RESULTS
  The obtained 10-fold cross-validated classification accuracy was 87.9%, sensitivity 90.0%, and specificity 86.7%, based on ten features. The area under the receiver operating characteristic curve (AUC) was 0.89.
  
  
  CONCLUSIONS
  The sulcal medial surface features can be used as biomarkers for cortical neuroanatomical abnormalities in AD. All the features were located in the left hemisphere, which had previously been reported to be more severely affected in AD and to lose grey matter faster than the right hemisphere.
authors.affiliation: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. Electronic address: mpl@hst.aau.dk.
authors.forename: Maciej
authors.initials: M
authors.lastname: Plocharski
authors.affiliation: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
authors.forename: Lasse Riis
authors.initials: LR
authors.lastname: Østergaard
authors.affiliation: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification*; D016022:Case-Control Studies; D002540:Cerebral Cortex / Q000473:pathology*; D005260:Female; D006801:Humans; D008297:Male; D060388:Support Vector Machine
keywords: Alzheimer's disease; Classification; MRI; SVM; Sulcal morphology