A Bayesian approach to multistage fitting of the variation of the skeletal age features.
Accurate assessment of skeletal maturity is important clinically. Skeletal age assessment is usually based on features encoded in ossification centers. Therefore, it is critical to design a mechanism to capture as much as possible characteristics of features. We have observed that given a feature, there exist stages of the skeletal age such that the variation pattern of the feature differs in these stages. Based on this observation, we propose a Bayesian cut fitting to describe features in response to the skeletal age. With our approach, appropriate positions for stage separation are determined automatically by a Bayesian approach, and a model is used to fit the variation of a feature within each stage. Our experimental results show that the proposed method surpasses the traditional fitting using only one line or one curve not only in the efficiency and accuracy of fitting but also in global and local feature characterization.
Hua
Dong
D
Department of Computer Science, The George Washington University, Washington, DC 20052, USA.
Chen
Dechang
D
Liu
Fang
F
Youssef
Abdou
A
D000365:Age Determination by Skeleton / Q000379:methods*; D000465:Algorithms; D001499:Bayes Theorem; D050278:Finger Phalanges / Q000000981:diagnostic imaging; D006225:Hand / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*
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