Abstract
A fully automatic iterative training approach for the generation of discriminative shape models for usage in the Generalized Hough Transform (GHT) is presented. The method aims at capturing the shape variability of the target object contained in the training data as well as identifying confusable structures (anti-shapes) and integrating this information into one model. To distinguish shape and anti-shape points and to determine their importance, an individual positive or negative weight is estimated for each model point by means of a discriminative training technique. The model is built from edge points surrounding the target point and the most confusable structure as identified by the GHT. Through an iterative approach, the performance of the model is gradually improved by extending the training dataset with images, where the current model failed to localize the target point. The proposed method is successfully tested on a set of 670 long-leg radiographs, where it achieves a localization rate of 74–97% for the respective tasks.
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Ruppertshofen, H., Lorenz, C., Schmidt, S. et al. Discriminative Generalized Hough transform for localization of joints in the lower extremities. Comput Sci Res Dev 26, 97–105 (2011). https://doi.org/10.1007/s00450-010-0137-x
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DOI: https://doi.org/10.1007/s00450-010-0137-x