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1 July 1990 Comparison of automatic atherosclerosis identification methods on MR imagery
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Abstract
Magnetic resonance imaging (MRI) provides excellent soft tissue contrast enabling the non-invasive visualization of soft lissue diseases. The quantification of tissues distinguishable in MR images significantly increases the diagnostic information available to physicians. New 3-D display workstations are available that can also make use of the tissue characteristics to generate clinically useful views of a patient. While simple tissue selection methods work with computed tomography (CT) images these same methods usually do not work with MR images. Several feasibility studies of tissue classification methods have been performed on MR images but few comparative studies of these methods have been published and little work is available on the best statistical model of tissues in MIRI. We have developed a novel method for the identification and quantification of soft tissues from MRI atherosclerosis in particular. This project is part of our work on the development of tissue characterization and identification tools to facilitate soft tissue disease diagnosis and evaluation utilizing MR imagery. Several supervised pattern recognition methods were investigated for tissue identification in MR images such as a Fisher linear discriminant and a minimum distance to the means classifier. For tissue in vivo adequate histology can be difficult to collect. We used cluster analysis methods to generate the necessary training information. ISODATA was modified to use hierarchical stopping rules to determine the true number of tissues in the images. This new method was
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Charles S. Carman and Michael B. Merickel "Comparison of automatic atherosclerosis identification methods on MR imagery", Proc. SPIE 1233, Medical Imaging IV: Image Processing, (1 July 1990); https://doi.org/10.1117/12.18895
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