Radiological bone age assessment is based on global or local image regions of interest (ROI), such as epiphyseal regions
or the area of carpal bones. Usually, these regions are compared to a standardized reference and a score determining the
skeletal maturity is calculated. For computer-assisted diagnosis, automatic ROI extraction is done so far by heuristic
approaches. In this work, we apply a high-level approach of scene analysis for knowledge-based ROI segmentation.
Based on a set of 100 reference images from the IRMA database, a so called structural prototype (SP) is trained. In this
graph-based structure, the 14 phalanges and 5 metacarpal bones are represented by nodes, with associated location,
shape, as well as texture parameters modeled by Gaussians. Accordingly, the Gaussians describing the relative
positions, relative orientation, and other relative parameters between two nodes are associated to the edges. Thereafter,
segmentation of a hand radiograph is done in several steps: (i) a multi-scale region merging scheme is applied to extract
visually prominent regions; (ii) a graph/sub-graph matching to the SP robustly identifies a subset of the 19 bones; (iii)
the SP is registered to the current image for complete scene-reconstruction (iv) the epiphyseal regions are extracted from
the reconstructed scene. The evaluation is based on 137 images of Caucasian males from the USC hand atlas. Overall,
an error rate of 32% is achieved, for the 6 middle distal and medial/distal epiphyses, 23% of all extractions need
adjustments. On average 9.58 of the 14 epiphyseal regions were extracted successfully per image. This is promising for
further use in content-based image retrieval (CBIR) and CBIR-based automatic bone age assessment.