Abstract
The Discriminative Generalized Hough Transform, a method for object localization and training of suitable models, is employed here for the localization of the femur in long-leg radiographs. The method is extended to improve the robustness of the procedure by integrating anatomical knowledge. This is achieved by combining the femur localization with the more robust knee localization. To this end, a region of interest is extracted from the original image, based on the result of the knee localization, in which the femur is initially localized. In addition, the procedure focuses on the target by extracting smaller regions of interest around the previously found coordinates and repeating the localization with smaller models. By this means, the precision of the first coarse localization is refined. The presented method is tested here on a large set of long-leg radiographs, where it achieves a localization rate of 94% on femurs not showing pathological conditions.
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© 2012 Springer-Verlag Berlin Heidelberg
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Boero, F., Ruppertshofen, H., Schramm, H. (2012). Femur Localization Using the Discriminative Generalized Hough Transform. In: Tolxdorff, T., Deserno, T., Handels, H., Meinzer, HP. (eds) Bildverarbeitung für die Medizin 2012. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28502-8_52
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DOI: https://doi.org/10.1007/978-3-642-28502-8_52
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