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Lookup NU author(s): Professor Bayan Sharif,
Dr Graeme Chester
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An automated knowledge-based vision system for skeletal growth estimation in children is reported in this paper. Images were obtained from hand radiographs of 32 male and 25 female children of age 1-16 yr. Phalanx bones were automatically localized and segmented using hierarchical inferences and active shape models, respectively. A number of shape descriptors were obtained from the segmented bone contour to quantify skeletal growth. From these descriptors, a feature vector was selected for a regression model and a Bayesian estimator. The estimation accuracy was 84% for females and 82% for males. This level of accuracy is comparable to that of expert pédiatrie radiologists, which suggests that the proposed approach has a potential application in pédiatrie medicine. © 2000 IEEE.
Author(s): Mahmoodi S, Sharif BS, Chester EG, Owen JP, Lee R
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Information Technology in Biomedicine
ISSN (print): 1089-7771
ISSN (electronic): 1558-0032
Publisher: Institution of Electronic and Electrical Engineers
PubMed id: 11206814
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