MR IMAGE SEGMENTATION USING VECTOR DECOMPOSITION AND PROBABILITY TECHNIQUES - A GENERAL-MODEL AND ITS APPLICATION TO DUAL-ECHO IMAGES

A general model is developed for segmenting magnetic resonance images using vector decomposition and probability techniques. Each voxel is a ssigned fractional volumes of q tissues from p differently weighted im ages (q less than or equal to p + 1) in the presence of partial-volume mixing, random noise, and other tissues. Compared with the eigenimage method, fewer differently weighted images are needed for segmenting t he q tissues, and the contrast-to-noise ratio in the calculated fracti onal volumes is improved. The model can produce composite tissue-type images similar to that of the probability methods, by comparing the fr actional volumes assigned to different tissues on each voxel. A three- tissue (p = 2, q = 3) model is illustrated for segmenting three tissue s from dual-echo images, It provides statistical analysis to the algeb raic method. A three-compartment phantom is segmented for validation. Two clinical examples are presented.