Mathematical segmentation of grey matter, white matter and cerebral spinalfluid from MR image pairs

The aims of this study were (1) to design a mathematical segmentation techn ique to allow extraction of grey matter, white matter and cerebral spinal f luid volumes from paired high resolution MR images and (2) to document the statistical accuracy of the method with different image combinations. A ser ies of linear equations were derived that describe proportional tissue volu mes in individual image voxels. The equations use estimates of pure tissue values to derive the proportion of each tissue within a single voxel. Repea tability of manual estimations of pure tissue values was assessed both usin g regions of interest and thresholding techniques. Statistical accuracy of tissue estimations for a variety of image pairs was assessed from measureme nts 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 li ttle contrast or the rank order of the signal intensities from pure tissue is reversed. A combination of inversion recovery fast spin echo and fast FL AIR 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 impr oves both of these figures to give a 1% statistical error on a 100 pixel sa mple.