Support Vector Machine Classification Based on Correlation Prototypes Applied to Bone Age Assessment

Bone age assessment (BAA) on hand radiographs is a frequent and time-consuming task in radiology. We present a method for (semi)automatic BAA which is done in several steps: 1) extract 14 epiphyseal regions from the radiographs; 2) for each region, retain image features using the image retrieval in medical application framework; 3) use these features to build a classifier model (training phase); 4) evaluate performance on cross-validation schemes (testing phase); 5) classify unknown hand images (application phase). In this paper, we combine a support vector machine (SVM) with cross correlation to a prototype image for each class. These prototypes are obtained choosing one random hand per class. A systematic evaluation is presented comparing nominal- and real-valued SVM with
k
nearest neighbor classification on 1097 hand radiographs of 30 diagnostic classes (0-19 years). Mean error in age prediction is 1.0 and 0.83 years for 5-NN and SVM, respectively. Accuracy of nominal- and real-valued SVM based on six prominent regions (prototypes) is 91.57% and 96.16%, respectively, for accepting about two years age range.