Image analysis and quantification of atherosclerosis using MRI.
This paper describes an image processing, pattern recognition, and computer graphics system for the noninvasive identification and evaluation of atherosclerosis using multidimensional Magnetic Resonance Imaging (MRI). Particular emphasis has been placed on the problem of developing a pattern recognition system for noninvasively identifying the different plaque classes involved in atherosclerosis using minimal a priori information. This pattern recognition technique involves an extension of the ISODATA clustering algorithm to include an information theoretic criterion (Consistent Akaike Information Criterion) to provide a measure of the fit of the cluster composition at a particular iteration to the actual data. A rapid 3-D display system is also described for the simultaneous display of multiple data classes resulting from the tissue identification process. This work demonstrates the feasibility of developing a "high information content" display which will aid in the diagnosis and analysis of the atherosclerotic disease process. Such capability will permit detailed and quantitative studies to assess the effectiveness of therapies, such as drug, exercise, and dietary regimens.
Merickel
M B
MB
Biomedical Engineering, University of Virginia, Charlottesville 22908.
Carman
C S
CS
Brookeman
J R
JR
Ayers
C R
CR
D000465:Algorithms; D001012:Aorta, Abdominal / Q000033:anatomy & histology / Q000473:pathology; D001161:Arteriosclerosis / Q000175:diagnosis*; D016000:Cluster Analysis; D003196:Computer Graphics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D012016:Reference Values