A Supervised Incremental Learning Technique for Automatic Recognition of the Skeletal Maturity, or can a Machine Learn to Assess Bone Age Without Radiological Training from Experts?
Abstract
Skeletal maturity estimation is an important medical procedure in the early diagnosis of growth disorders. Traditionally, it is performed by an expert physician or radiologist who determines it based on a visual subjective inspection, the approximated bone age of the child. This task is time consuming and is usually dependent on the judgment of each particular physician. Therefore, automated methods are extremely valuable and desirable. In this paper, we propose and describe an automatic method to estimate skeletal maturity through a supervised and incremental learning approach. Our method determines bone age by comparing aligned images with a K–NN regression classifier. Here, we have solved the difficult task of image alignment by designing a radiological-hand specific Active Appearance Model, which was developed from a varied set of hand-labeled radiological images. By using this active model, our system constructs its own learned database by increasing a set of shape-aligned training images which are incrementally stored. Thus, when a test image arrives at the system, the alignment process is performed before the classification task takes place. For that purpose, we designed an original layout of landmarks to be located in representative regions of the radiographical image of the hand. Our results show that it is possible to use pixels directly as classification features as long as training and testing images have been previously aligned in shape and pose.
