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 assigned fractional volumes of q tissues from p differently weighted images (q < or = 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 the q tissues, and the contrast-to-noise ratio in the calculated fractional volumes is improved. The model can produce composite tissue-type images similar to that of the probability methods, by comparing the fractional volumes assigned to different tissues on each voxel. A three-tissue (p = 2, q = 3) model is illustrated for segmenting three tissues from dual-echo images. It provides statistical analysis to the algebraic method. A three-compartment phantom is segmented for validation. Two clinical examples are presented.
Kao
Y H
YH
Department of Physics, University of Wisconsin-Madison, USA.
Sorenson
J A
JA
Winkler
S S
SS
D016477:Artifacts; D001921:Brain / Q000473:pathology; D001929:Brain Edema / Q000175:diagnosis / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis / Q000473:pathology; D017352:Echo-Planar Imaging / Q000295:instrumentation*; D005583:Fourier Analysis; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation*; D008432:Mathematical Computing; D008953:Models, Anatomic; D011336:Probability; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012984:Software