A Unified Framework for Disease Classification

This talk discusses an anatomical parameterization of spatial warps to reveal structural differences between medical images of healthy control subjects and disease patients. The warps are represented as structure-specific 9-parameter affine transformations, which constitute a global, non-rigid mapping between the atlas and image coordinates. Our method estimates the structure specific transformation parameters directly from medical scans by minimizing a Kullback-Leibler divergence measure. The resulting parameters are then input to a linear Support Vector Machine classifier, which assigns individual scans to a specific clinical group. We test the accuracy of our approach on a data set consisting of Magnetic Resonance scans from 16 first episode schizophrenics and 17 age-matched healthy control subjects. On this small size data set, our approach, which performs classification based on the MR images directly, yields a leave-one-out cross-validation accuracy of up to 90%. This compares favorably with the accuracy achieved by state-of-the-art techniques in schizophrenia MRI research.