that arises the surface coil array.

This entropy-based method does not require classification and

robustly addresses some problems that are more severe than

those found in brain imaging, including noise, steep bias field,

sensitivity of artery wall voxels to edge artifacts, and signal voids

near the artery wall. Validation studies were performed on a

synthetic digital phantom with realistic intensity inhomogeneity,

a physical phantom roughly mimicking the neck, and patient

carotid artery images. We compared LEMS to a modified fuzzy

c-means segmentation based method (mAFCM), and a linear

filtering method (LINF). Following LEMS correction, skeletal

muscles in patient images were relatively isointense across the field

of view. In the physical phantom, LEMS reduced the variation

in the image to 1.9 and across the vessel wall region to 2.5 ,

a value which should be sufficient to distinguish-that arises　the surface coil array.

This entropy-based method does not require classification and

robustly addresses some problems that are more severe than

those found in brain imaging, including noise, steep bias field,

sensitivity of artery wall voxels to edge artifacts, and signal voids

near the artery wall. Validation studies were performed on a

synthetic digital phantom with realistic intensity inhomogeneity,

a physical phantom roughly mimicking the neck, and patient

carotid artery images. We compared LEMS to a modified fuzzy

c-means segmentation based method (mAFCM), and a linear

filtering method (LINF). Following LEMS correction, skeletal

muscles in patient images were relatively isointense across the field

of view. In the physical phantom, LEMS reduced the variation

in the image to 1.9　and across the vessel wall region to 2.5 ,

a value which should be sufficient to distinguish