Distance Metric Learning as Feature Reduction Technique for the Alzheimer’s Disease Diagnosis
ABSTRACT In this paper we present a novel classification method of SPECT images for the development of a computer aided diagnosis (CAD)
system aiming to improve the early detection of the Alzheimer’s Disease (AD). The system combines firstly template-based normalized
mean square error (NMSE) features of tridimensional Regions of Interest (ROIs) t-test selected with secondly Large Margin
Nearest Neighbors (LMNN), which is a distance metric technique aiming to separate examples from different classes (Controls
and AD) by a Large Margin. LMNN uses a rectangular matrix (called RECT-LMNN) as an effective feature reduction technique.
Moreover, the proposed system evaluates Support Vector Machine (SVM) classifier, yielding a 97.93% AD diagnosis accuracy,
which reports clear improvements over existing techniques, for instance the Principal Component Analysis (PCA), Linear Discriminant
Analysis (LDA) or Normalized Minimum Squared Error (NMSE) evaluated with SVM.
KeywordsSPECT Brain Imaging–Alzheimer’s disease–Distance Metric Learning–feature reduction–Support Vector Machines