Bone Age Pre-estimation Using Partial Least Squares Regression Analysis with a Priori Knowledge

Computerized bone age estimation was widely developed during near decade. There are many techniques were applied to this filed. In the study, a new concept, the partial least squares (PLS) regression analysis was proposed to pre-estimate the bone age as a priori knowledge. PLS is a multivariate statistical analysis method and can find combinations of the predictors that have a large covariance with the response values. One hundred and eleven radiograms with the third finger of the left hand were retrieved from DICOM system of Department of Radiology, Taipei Veterans General Hospital. The projection data of radiograms with the third finger were calculated, and then the normalized process was implemented. The discrete cosine transform (DCT) was employed to obtain components from projection data as features in this study. The first 30 components of DCT were selected as features. PLS was utilized to separate these features into four stages of skeleton maturation of children as a new application. The result of PLS regression presented that the average residual is 0.5. The study demonstrated a significant result that the PLS can successfully pre-estimate the bone age as a priori knowledge.