Mathematical segmentation of grey matter, white matter and cerebral spinal fluid from MR image pairs.

Abstract

The aims of this study were (1) to design a mathematical segmentation technique to allow extraction of grey matter, white matter and cerebral spinal fluid volumes from paired high resolution MR images and (2) to document the statistical accuracy of the method with different image combinations. A series of linear equations were derived that describe proportional tissue volumes in individual image voxels. The equations use estimates of pure tissue values to derive the proportion of each tissue within a single voxel. Repeatability of manual estimations of pure tissue values was assessed both using regions of interest and thresholding techniques. Statistical accuracy of tissue estimations for a variety of image pairs was assessed from measurements of root-mean-square noise and mean grey level intensity. The technique was used to produce parametric images of grey and white matter distribution. The segmentation technique showed greatest statistical accuracy when the first image has high grey/white matter contrast and the second image has little contrast or the rank order of the signal intensities from pure tissue is reversed. A combination of inversion recovery fast spin echo and fast FLAIR images produced a statistical error of 11% for grey matter and 10% for white matter for any given voxel. The effect of increasing sample size improves both of these figures to give a 1% statistical error on a 100 pixel sample.