Bone Age Assessment: Traditional Machine Learning Methods & References
There are several traditional machine learning methods for bone age assessment, including regression analysis, neural networks, and support vector machines. These methods use various features of the bones, such as shape, size, and texture, to predict the age of a person based on X-ray images.
Some references for traditional machine learning methods for bone age assessment include:
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Tanner J, Whitehouse R. Clinical longitudinal standards for height, weight, height velocity, weight velocity, and stages of puberty. Arch Dis Child. 1976;51(3):170-179. doi:10.1136/adc.51.3.170
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Greulich WW, Pyle SI. Radiographic atlas of skeletal development of the hand and wrist. Stanford University Press; 1959.
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Thodberg HH. A software system for bone age assessment. Proc SPIE Int Soc Opt Eng. 2007;6512:65120A. doi:10.1117/12.709846
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Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang HK. Computer-assisted bone age assessment: Image preprocessing and segmentation. J Digit Imaging. 2010;23(3):279-289. doi:10.1007/s10278-009-9227-5
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Lee H, Grosse R, Ranganath R, Ng AY. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning. 2009:609-616.
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