Magnetic Resonance (MR) images can be considered as multispectral images so that MR imaging can be processed by
multispectral imaging techniques such as maximum likelihood classification. Unfortunately, most multispectral imaging
techniques are not particularly designed for target detection. On the other hand, hyperspectral imaging is primarily
developed to address subpixel detection, mixed pixel classification for which multispectral imaging is generally not
effective. This paper takes advantages of hyperspectral imaging techniques to develop target detection algorithms to find
lesions in MR brain images. Since MR images are collected by only three image sequences, T1, T2 and PD, if a
hyperspectral imaging technique is used to process MR images it suffers from the issue of insufficient dimensionality.
To address this issue, two approaches to nonlinear dimensionality expansion are proposed, nonlinear correlation
expansion and nonlinear band ratio expansion. Once dimensionality is expanded hyperspectral imaging algorithms are
readily applied. The hyperspectral detection algorithm to be investigated for lesion detection in MR brain is the well-known
subpixel target detection algorithm, called Constrained Energy Minimization (CEM). In order to demonstrate the
effectiveness of proposed CEM in lesion detection, synthetic images provided by BrainWeb are used for experiments.