Knowledge-based segmentation and feature analysis of hand and wrist radiographs

Nicholas David Efford

doi:10.1117/12.148672

29 July 1993 Knowledge-based segmentation and feature analysis of hand and wrist radiographs

Nicholas David Efford

Proceedings Volume 1905, Biomedical Image Processing and Biomedical Visualization; (1993) https://doi.org/10.1117/12.148672
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

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.

Citation Download Citation

Nicholas David Efford "Knowledge-based segmentation and feature analysis of hand and wrist radiographs", Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); https://doi.org/10.1117/12.148672

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