Extracting Discriminative Information from Medical Images: A Multivariate Linear Approach

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 analyze 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 analyzing segmented structures individually or performing hypothesis tests on each feature separately. In this paper, we present a general multivariate linear framework to identify and analyze the most discriminating hyper-plane separating two populations. The goal is to analyze all the intensity features simultaneously rather than segmented versions of the data separately or feature-by-feature. The conceptual and mathematical simplicity of the approach, which pivotal step is spatial normalization, involves the same operations irrespective of the complexity of the experiment or nature of the data, giving multivariate results that are easy to interpret. To demonstrate its performance we present experimental results on artificially generated data set and real medical data

[1]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

[2]  Ja-Chen Lin,et al.  A new LDA-based face recognition system which can solve the small sample size problem , 1998, Pattern Recognit..

[3]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[4]  W. Eric L. Grimson,et al.  Detection and analysis of statistical differences in anatomical shape , 2005, Medical Image Anal..

[5]  Jian Yang,et al.  Why can LDA be performed in PCA transformed space? , 2003, Pattern Recognit..

[7]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[8]  Duncan Fyfe Gillies,et al.  A Maximum Uncertainty LDA-Based Approach for Limited Sample Size Problems : With Application to Face Recognition , 2005, SIBGRAPI.

[9]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[10]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[11]  Karl J. Friston,et al.  Generative and recognition models for neuroanatomy , 2004, NeuroImage.

[12]  Griselda J. Garrido,et al.  A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease , 2003, Neurobiology of Aging.

[13]  A. Toga,et al.  Cortical variability and asymmetry in normal aging and Alzheimer's disease. , 1998, Cerebral cortex.

[14]  Carlos E. Thomaz,et al.  A new covariance estimate for Bayesian classifiers in biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Karl J. Friston,et al.  A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains , 2001, NeuroImage.

[16]  Dinggang Shen,et al.  Morphological classification of brains via high-dimensional shape transformations and machine learning methods , 2004, NeuroImage.