title: Segmentation of small structures in MR images: semiautomated tissue hydration measurement.
abstract: 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.
authors.affiliation: Center for Non-Invasive Diagnosis, School of Medicine, University of New Mexico, Albuquerque 87131, USA.
authors.forename: D
authors.initials: D
authors.lastname: Koechner
authors.forename: H
authors.initials: H
authors.lastname: Petropoulos
authors.forename: R P
authors.initials: RP
authors.lastname: Eaton
authors.forename: B L
authors.initials: BL
authors.lastname: Hart
authors.forename: W M
authors.initials: WM
authors.lastname: Brooks
mesh_terms: D000465:Algorithms; D001331:Automation; D001834:Body Water / Q000378:metabolism*; D003929:Diabetic Neuropathies / Q000175:diagnosis / Q000378:metabolism; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011237:Predictive Value of Tests; D013497:Sural Nerve / Q000033:anatomy & histology* / Q000378:metabolism