Automated classification system for Bone Age X-ray images
Jinwoo Seok, Baro Hyun, Joséphine Z. Kasa-Vubu, Anouck Girard · 2012
Bone Age (BA) determination using radiological images of left hands and wrists is important in pediatric endocrinology to correctly assess growth and pubertal maturation. In this paper, we propose a fully automated Greulich and Pyle Atlas (GP) bone age determination system using feature extraction and machine learning classifiers. The original contributions of this paper are as follows: (i) We use commercially available morphing tools to create a modified GP atlas that has images regularly spaced at three month intervals, (ii) We propose a novel Singular Value Decomposition (SVD) based feature extractor to create a feature vector. We use the Scale Invariant Feature Transform (SIFT) to extract features from the images then apply SVD to compose the feature vectors. Then, we train a Neural Network classifier using the generated feature vectors. Our preliminary results show that, even with a small number of training data sets, we obtain promising results. Future direction is discussed.