Multispectral magnetic resonance image analysis using principal component and linear discriminant analysis

Abstract

Purpose: To explore the possibilities of combining multispectral magnetic resonance (MR) images of different patients within one data matrix.

Materials and methods: Principal component and linear discriminant analysis was applied to multispectral MR images of 12 patients with different brain tumors. Each multispectral image consisted of T1-weighted, T2-weighted, proton-density-weighted, and gadolinium-enhanced T1-weighted MR images, and a calculated relative regional cerebral blood volume map.

Results: Similar multispectral image regions were clustered, while dissimilar multispectral image regions were scattered in a single plot. Both principal component and linear discriminant analysis allowed discrimination between healthy and tumor regions on the image. In addition, linear discriminant analysis allowed discrimination between oligodendrogliomas and astrocytomas. However, the discriminant analysis method was partially capable of recognizing the tumor identity in unknown multispectral images.

Conclusion: The proposed method may help the radiologist in comparing multispectral MR images of different patients in a more easy and objective way.