Multivariate Statistical Differences of MRI Samples of the Human Brain

Authors:
Carlos E. Thomaz;Fabio L. Duran;Geraldo F. Busatto;Duncan F. Gillies;Daniel Rueckert
Affiliations:
Department of Electrical Engineering, Centro Universitário da FEI, São Paulo, Brazil;Departments of Psychiatry and Radiology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil;Departments of Psychiatry and Radiology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil;Department of Computing, Imperial College, London, UK;Department of Computing, Imperial College, London, UK
Venue:
Journal of Mathematical Imaging and Vision
Year:
2007

Citing 4
Cited 2

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
A multivariate statistical analysis of the developing human brain in preterm infants

Image and Vision Computing
A new covariance estimate for Bayesian classifiers in biometric recognition

IEEE Transactions on Circuits and Systems for Video Technology

Exploring the knowledge contained in neuroimages: Statistical discriminant analysis and automatic segmentation of the most significant changes

Artificial Intelligence in Medicine
A Diffeomorphic Mapping Based Characterization of Temporal Sequences: Application to the Pelvic Organ Dynamics Assessment

Journal of Mathematical Imaging and Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multivariate statistical discrimination methods are suitable not only for classification but also for characterization of differences between a reference group of patterns and the population under investigation. In the last years, statistical methods have been proposed to classify and analyse morphological and anatomical structures of medical images. Most of these techniques work in high-dimensional spaces of particular features such as shapes or statistical parametric maps and have overcome the difficulty of dealing with the inherent high dimensionality of medical images by analysing segmented structures individually or performing hypothesis tests on each feature separately. In this paper, we present a general multivariate linear framework that addresses the small sample size problem in medical images. The goal is to identify and analyse the most discriminating hyper-plane separating two populations using all the intensity features simultaneously rather than segmented versions of the data separately or feature-by-feature. To demonstrate the performance of the multivariate linear framework we carry out experimental results on artificially generated data set and on a real medical data composed of magnetic resonance images (MRI) of subjects suffering from Alzheimer's disease (AD) compared to an elderly healthy control group. To our knowledge this is the first multivariate statistical analysis of the human brain in AD that uses the whole features (texture + shapes) simultaneously rather than segmented version of the images. The conceptual and mathematical simplicity of the approach involves the same operations irrespective of the complexity of the experiment or nature of the spatially normalized data, giving multivariate results that are plausible and easy to interpret by the clinicians.