Lesion Detection in Magnetic Resonance Brain Images by Hyperspectral Imaging Algorithms

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.