Kernel-based image representation for brain MRI discrimination

Abstract

Discriminating Magnetic Resonance Images (MRI) allows supporting the analysis of physiological and pathological processes, however, finding MRI relationships posses a challenge when analyzing in voxel-based high-dimensional spaces. We introduce a kernel-based representation approach to support MRI discrimination. In this sense, inherent Inter-Slice Kernel relationship is employed to highlight brain structure distributions. Then, a generalized Euclidean metric is estimated by using a kernel-based centered alignment algorithm to code the correlation between MRI dependencies and prior demographic patient information. The proposed approach is tested on MRI data classification by considering patient gender and age categories. Attained results show that proposed methodology improves data interpretability and separability in comparison to state of the art algorithms based on MRI Voxel-wise features. Therefore, introduced kernel-based representation can be useful to support MRI clustering and similarity inference tasks required on template-based image segmentation and atlas construction.