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14 February 2012 A multi-dimensional model for localization of highly variable objects
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Abstract
In this work, we present a new type of model for object localization, which is well suited for anatomical objects exhibiting large variability in size, shape and posture, for usage in the discriminative generalized Hough transform (DGHT). The DGHT combines the generalized Hough transform (GHT) with a discriminative training approach to automatically obtain robust and efficient models. It has been shown to be a strong tool for object localization capable of handling a rather large amount of shape variability. For some tasks, however, the variability exhibited by different occurrences of the target object becomes too large to be represented by a standard DGHT model. To be able to capture such highly variable objects, several sub-models, representing the modes of variability as seen by the DGHT, are created automatically and are arranged in a higher dimensional model. The modes of variability are identified on-the-fly during training in an unsupervised manner. Following the concept of the DGHT, the sub-models are jointly trained with respect to a minimal localization error employing the discriminative training approach. The procedure is tested on a dataset of thorax radiographs with the target to localize the clavicles. Due to different arm positions, the posture and arrangement of the target and surrounding bones differs strongly, which hampers the training of a good localization model. Employing the new model approach the localization rate improves by 13% on unseen test data compared to the standard model.
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Heike Ruppertshofen, Thomas Bülow, Jens von Berg, Sarah Schmidt, Peter Beyerlein, Zein Salah, Georg Rose, and Hauke Schramm "A multi-dimensional model for localization of highly variable objects", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142L (14 February 2012); https://doi.org/10.1117/12.910900
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