Bone age estimation based on phalanx information with fuzzy constrain of carpals.
The proposed automatic bone age estimation system was based on the phalanx geometric characteristics and carpals fuzzy information. The system could do automatic calibration by analyzing the geometric properties of hand images. Physiological and morphological features are extracted from medius image in segmentation stage. Back-propagation, radial basis function, and support vector machine neural networks were applied to classify the phalanx bone age. In addition, the proposed fuzzy bone age (BA) assessment was based on normalized bone area ratio of carpals. The result reveals that the carpal features can effectively reduce classification errors when age is less than 9 years old. Meanwhile, carpal features will become less influential to assess BA when children grow up to 10 years old. On the other hand, phalanx features become the significant parameters to depict the bone maturity from 10 years old to adult stage. Owing to these properties, the proposed novel BA assessment system combined the phalanxes and carpals assessment. Furthermore, the system adopted not only neural network classifiers but fuzzy bone age confinement and got a result nearly to be practical clinically.
Hsieh
Chi-Wen
CW
Department of Electrical Engineering, National Tsing Hwa University, Hsinchu, Taiwan, ROC. hcw@phy.ncu.edu.tw
Jong
Tai-Lang
TL
Tiu
Chui-Mei
CM
D000293:Adolescent; D000365:Age Determination by Skeleton / Q000379:methods*; D002348:Carpal Bones / Q000000981:diagnostic imaging*; D002648:Child; D002675:Child, Preschool; D005260:Female; D050278:Finger Phalanges / Q000000981:diagnostic imaging*; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007223:Infant; D008297:Male; D016571:Neural Networks, Computer