Chi-Wen Hsieh1, Tai-Lang Jong, Yi-Hong Chou, Chui-Mei Tiu. 1. Department of Physics, National Central University, Chungli, and Department of Radiology, Taipei Veterans General Hospital, Taiwan, China.
Abstract
BACKGROUND: Bone age development is one of the significant indicators depicting the growth status of children. However, bone age assessment is an heuristic and tedious work for pediatricians. We developed a computerized bone age estimation system based on the analysis of geometric features of carpal bones. METHODS: The geometric features of carpals were extracted and analyzed to judge the bone age of children by computerized shape and area description. Four classifiers, linear, nearest neighbor, back-propagation neural network, and radial basis function neural network, were adopted to categorize bone age. Principal component and discriminate analyses were employed to improve assorting accuracy. RESULTS: The hand X-ray films of 465 boys and 444 girls served as our database. The features were extracted from carpal bone images, including shape, area, and sequence. The proposed normalization area ratio method was effective in bone age classification by simulation. Besides, features statistics showed similar results between the standard of the Greulich and Pyle atlas and our database. CONCLUSIONS: The bone area has a higher discriminating power to judge bone age. The ossification sequence of trapezium and trapezoid bones between Taiwanese and the atlas of the GP method is quite different. These results also indicate that carpal bone assessment with classification of neural networks can be correct and practical.
BACKGROUND: Bone age development is one of the significant indicators depicting the growth status of children. However, bone age assessment is an heuristic and tedious work for pediatricians. We developed a computerized bone age estimation system based on the analysis of geometric features of carpal bones. METHODS: The geometric features of carpals were extracted and analyzed to judge the bone age of children by computerized shape and area description. Four classifiers, linear, nearest neighbor, back-propagation neural network, and radial basis function neural network, were adopted to categorize bone age. Principal component and discriminate analyses were employed to improve assorting accuracy. RESULTS: The hand X-ray films of 465 boys and 444 girls served as our database. The features were extracted from carpal bone images, including shape, area, and sequence. The proposed normalization area ratio method was effective in bone age classification by simulation. Besides, features statistics showed similar results between the standard of the Greulich and Pyle atlas and our database. CONCLUSIONS: The bone area has a higher discriminating power to judge bone age. The ossification sequence of trapezium and trapezoid bones between Taiwanese and the atlas of the GP method is quite different. These results also indicate that carpal bone assessment with classification of neural networks can be correct and practical.