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Subsequently, mode membership (image or microarrays) in the 20 feature set was examined for the two schemes: top-means and top-20.
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Thereafter, were the membership images combined through a weighted linear combination.
The T1-weighted MPRAGE images were co-registered to a template atlas, inhomogeneitycorrected, masked and segmented through a fuzzy c-means cluster algorithm into grey matter, white matter and cerebrospinal fluid membership images using the in-house software BMAP/Volstat [12].
A fuzzy set is defined, so that the degree of membership of each image to this fuzzy set is related to the user's interest in that image [15].
It used to be common for synagogues to put a cap on their membership, furthering the image of Judaism as a private club.
It provides better generalization capability by using the distance between local feature and certain class to estimate image membership.
During the process of clustering, similarities between SPNs are to be used to determine the class membership of each image (or SPN).
The user's feedback, both positive and negative, are then used to determine the degree of membership of each image to set being analyzed.
Imaging data were identified only by coded numbers and group membership was coded during image analysis.
Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed.
Raters blinded to group membership visually inspected the images to exclude those evidencing excessive motion.
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