<title>Knowledge-based segmentation and feature analysis of hand and wrist radiographs</title>

Abstract

The segmentation of hand and wrist radiographs for applications such as skeletal maturity assessment is best achieved by model-driven approaches incorporating anatomical knowledge. The reasons for this are discussed, and a particular frame-based or 'blackboard' strategy for the simultaneous segmentation of the hand and estimation of bone age via the TW2 method is described. The new approach is structured for optimum robustness and computational efficiency: features of interest are detected and analyzes in order of their size and prominence in the image, the largest and most distinctive being dealt with first, and the evidence generated by feature analysis is used to update a model of hand anatomy and hence guide later stages of the segmentation. Closed bone boundaries are formed by a hybrid technique combining knowledge-based, one-dimensional edge detection with model-assisted heuristic tree searching.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.