Bone age cluster assessment and feature clustering analysis based on phalangeal image rough segmentation

There are different feature selections in a bone age assessment (BAA) system for various stages of skeletal development. For example, diameters of epiphysis and metaphysis are used as sensitive factors during the early stage. Once the epiphyseal fusion has started, an additional feature such as the degree of fusion is extracted at the later stage. Image analysis is a critical point for feature selections to get a fine BAA, which includes ROI processing and feature extraction. Nevertheless, the related modeling techniques are various depending on the characteristics of different stages of bone maturity, which usually are taken as a priori knowledge in most previously proposed schemes. If a coarse bone age cluster (stage) for a hand radiograph could be automatically pre-assigned, then these corresponding image analysis methods can be identified. This could avoid taking a priori knowledge and provide a more flexible and reliable BAA system. For this purpose, a bone age cluster assessment system using fuzzy neural network (FNN) based on phalangeal image rough segmentation is presented in this work. This system includes two parts. The first part adjusts the feature weights to stable conditions according to four new defined bone age stages, which satisfy feature development of epiphysis and metaphysis. The second part is bone age cluster assessment on hand radiography based on the results of the first part. Experimental results reveal that the presented FNN system provides a very good ability to assign a hand radiograph to an appropriate bone age cluster and demonstrates the rationality of those new defined stages. Furthermore, the related feature clustering analysis for various stages is discussed to provide an accurate quantitative evaluation of specific features for the final BAA.

► Our bone age cluster assessment system using (FNN) could avoid taking a priori knowledge.
► The presented approach is a flexible, stable, and reliable classification method.
► Our approach can avoid the unsuitable feature selection of the original two-stage clustering.
► A feature clustering analysis for different bone age cluster is presented.