A multispectral analysis of brain tissues.

Abstract

With the increasing use of three-dimensional MRI techniques it is becoming necessary to explore automated techniques for locating pathology in the volume images. The suitability of a specific technique to locate and identify healthy tissues of the brain was examined as a first step toward eventually identifying pathology in images. This technique, called multispectral image segmentation, is based on the classification of tissue types in an image according to their characteristics in various spectral regions. The spectral regions chosen for this study were the hydrogen spin-lattice relaxation time T1, spin-spin relaxation time T2, and spin density, rho. Single-echo, spin-echo magnetic resonance images of axial slices through the brain at the level of the lateral ventricles were recorded on a 1.5 Tesla imager from 20 volunteers ranging in age from 17 to 72 years. These images were used to calculate the T1, T2, and rho images used for the classification. Tissue classification was performed by locating clusters of pixels in a three-dimensional T1(-1)-T2(-1)-rho histogram. Gray matter, white matter, cerebrospinal fluid, meninges, muscle, and adipose tissues were readily classified in magnetic resonance images of the volunteers with a single set of T1, T2, and rho values. Cluster characteristics, such as size, shape, and location, provided information on the imaging procedure and tissue characteristics.