Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

Abstract

Abstract-This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the brain in comparison to the normal template anatomy. The sparse data enables efficient data reduction and classification via the penalised linear discriminant analysis with resampling. The classification accuracy obtained in an experiment with magnetic resonance brain images of first episode schizophrenia patients and healthy controls is comparable to the related state-of-the-art studies.

Random Subspace Ensemble Artificial Neural Networks for First-episode Schizophrenia Classification

“…We used the same dataset as in [22]. It consisted of MRI data of 52 schizophrenia patients and 52 age-and sexmatched (only men) healthy control subjects without family history or personal history of axis I psychiatric conditions.…”