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Skeletal growth estimation using radiographie image processing and analysis

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.

Publication metadata

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

Year: 2000

Volume: 4

Issue: 4

Pages: 292-297

ISSN (print): 1089-7771

ISSN (electronic): 1558-0032

Publisher: Institution of Electronic and Electrical Engineers


DOI: 10.1109/4233.897061

PubMed id: 11206814


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