Key result
Local entropy minimization with a bicubic spline model (LEMS) reduced image variation to 1.9% and vessel wall region variation to 2.5% in a physical phantom.
Population
Synthetic digital phantom, physical phantom mimicking the neck, and patient carotid artery images
Comparison
Local entropy minimization with a bicubic spline… vs Modified fuzzy c-means segmentation based…
Authors
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May aid MR plaque characterization; leaves open clinical validation in patients.
The LEMS method effectively corrects intensity inhomogeneity in MR images, showing promise for aiding human and computerized tissue classification of atherosclerotic plaques.
Salvado et al. (2006) studied Atherosclerotic disease. Local entropy minimization with a bicubic spline model (LEMS) vs. Modified fuzzy c-means segmentation based method (mAFCM) and a linear filtering method (LINF) was evaluated on Variation in the image and across the vessel wall region. Local entropy minimization with a bicubic spline model (LEMS) reduced image variation to 1.9% and vessel wall region variation to 2.5% in a physical phantom.
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