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Segmentation of small structures in MR images: Semiautomated tissue hydration measurement
Author(s) -
Koechner Donna,
Petropoulos Helen,
Eaton R. Philip,
Hart Blaine L.,
Brooks William M.
Publication year - 1995
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.1880050320
Subject(s) - neuroradiologist , segmentation , computer science , noise (video) , thresholding , sural nerve , artificial intelligence , image segmentation , image processing , edge detection , magnetic resonance imaging , pattern recognition (psychology) , computer vision , medicine , image (mathematics) , radiology , anatomy
Segmentation of small anatomic structures In noisy magnetic resonance (MR) images is inherently challenging because the edge Information is contained in the same high‐frequency image component as the noise. The authors overcame this obstacle in the analysis of the sural nerve in the ankle by processing images to reduce noise and extracting edges with an edge detection algorithm less sensitive to noise. Anatomic accuracy of the segmentation was confirmed by a neuroradiologist. A nerve hydration coefficient was determined from the signal intensity of the nerve in these segmented images. These semiautomated measurements of hydration agreed closely with those obtained with a previously described manual method ( n =44, P =.76). Each image in the study was analyzed identically, with no modification of the computer algorithm parameters. The data suggest that this robust method may be useful in a multicenter evaluation of diabetes treatment protocols.