Using a maximum uncertainty LDA-based approach to classify and analyse MR brain images

Multivariate statistical learning techniques that analyse all voxels simultaneously have been used to classify and describe PAR brain images. Most of these techniques have overcome the difficulty of dealing with the inherent high dimensionality of 3D brain image data by using pre-processed segmented images or a number of specific features. However, an intuitive way of mapping the classification results back into the original image domain for further interpretation remains challenging. In this paper, we introduce the idea of using Principal Components Analysis (PCA) plus the maximum uncertainty Linear Discriminant Analysis (LDA) based approach to classify and analyse magnetic resonance (MR) images of the brain. It avoids the computation costs inherent in commonly used optimisation processes, resulting in a simple and efficient implementation for the maximisation and interpretation of the Fisher's classification results. In order to demonstrate the effectiveness of the approach, we have used two MR brain data sets. The first contains images of 17 schizophrenic patients and 5 controls, and the second is composed of brain images of 12 preterm infants at term equivalent age and 12 term controls. The results indicate that the two-stage linear classifier not only makes clear the statistical differences between the control and patient samples, but also provides a simple method of analysing the results for further medical research.