Abstract
We present a discriminative approach to the Generalized Hough Transform (GHT) employing a novel fully-automated training procedure for the estimation of discriminative shape models. The technique aims at learning the shape and variability of the target object as well as further confusable structures (anti-shapes), visible in the training images. The integration of the learned target shape and anti-shapes into a single GHT model is implemented straightforwardly by positive and negative weights. These weights are learned by a discriminative training and utilized in the GHT voting procedure. In order to capture the shape and anti-shape information from a set of training images, the model is built from edge structures surrounding the correct and the most confusable locations. In an iterative procedure, the training set is gradually enhanced by images from the development set on which the localization failed. The proposed technique is shown to substantially improve the object localization capabilities on long-leg radiographs.
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References
Ballard, D.H.: Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognit. 13(2), 111–122 (1981)
Beyerlein, P.: Discriminative Model Combination. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 481–484 (1998)
Deselaers, T., Keysers, D., Ney, H.: Improving a Discriminative Approach to Object Recognition using Image Patches. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 326–333. Springer, Heidelberg (2005)
Gooßen, A., Hermann, E., Gernoth, T., Pralow, T., Grigat, R.-R.: Model-Based Lower Limb Segmentation Using Weighted Multiple Candidates. In: Bildverarbeitung für die Medizin, pp. 276–280. Springer, Heidelberg (2010)
Heimann, T., van Ginneken, B., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P., Ying, C., Cordova, A., Dawant, B.M., Fidrich, M., Furst, J.D., Furukawa, D., Grenacher, L., Hornegger, J., Kainmuller, D., Kitney, R.I., Kobatake, H., Lamecker, H., Lange, T., Lee, J., Lennon, B., Li, R., Li, S., Meinzer, H.-P., Nemeth, G., Raicu, D.S., Rau, A.-M., van Rikxoort, E.M., Rousson, M., Rusko, L., Saddi, K.A., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Waite, J.M., Wimmer, A., Wolf, I.: Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets. IEEE Trans. Med. Imaging 28(8), 1251–1265 (2009)
Heimann, T., Münziger, S., Meinzer, H.-P., Wolf, I.: A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation. In: Information Processing in Medical Imaging, pp. 1–12 (2007)
Jaynes, E.T.: Information Theory and Statistical Mechanics. Phys. Rev. 106(4), 620–630 (1957)
Lee, Y., Hara, T., Fujita, H., Itoh, S., Ishigaki, T.: Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans. Med. Imaging 20(7), 595–604 (2001)
Leibe, B., Leonardis, A., Schiele, B.: Robust Object Detection with Interleaved Categorization and Segmentation. Int. J. Computer Vis. 77(1-3), 259–289 (2008)
Linguraru, M.G., Vercauteren, T., Reyes-Aguirre, M., González Ballester, M.A., Ayache, N.: Segmentation Propagation from Deformable Atlases for Brain Mapping and Analysis. Brain Research Journal 1(4) (2007)
Maji, S., Malik, J.: Object Detection using a Max-Margin Hough Transform. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1038–1045 (2009)
MartÃn Recuero, A.B., Beyerlein, P., Schramm, H.: Discriminative Optimization of 3D Shape Models for the Generalized Hough Transform. In: 7th International Conference and Workshop on Ambient Intelligence and Embedded Systems (2008)
Ruppertshofen, H., Lorenz, C., Beyerlein, P., Salah, Z., Rose, G., Schramm, H.: Fully Automatic Model Creation for Object Localization utilizing the Generalized Hough Transform. In: Bildverarbeitung für die Medizin, pp. 281–285. Springer, Heidelberg (2010)
Schramm, H., Ecabert, O., Peters, J., Philomin, V., Weese, J.: Towards Fully Automatic Object Detection and Segmentation. In: SPIE Medical Imaging, p. 614402 (2006)
Seghers, D., Slagmolen, P., Lambelin, Y., Hermans, J., Loeckx, D., Maes, F., Suetens, P.: Landmark based liver segmentation using local shape and local intensity models. In: MICCAI Workshop on 3D Segmentation in the Clinic: a Grand Challenge, pp. 135–142 (2007)
Zheng, Y., Georgescu, B., Comaniciu, D.: Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 411–422. Springer, Heidelberg (2009)
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Ruppertshofen, H. et al. (2011). Iterative Training of Discriminative Models for the Generalized Hough Transform. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2010. Lecture Notes in Computer Science, vol 6533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18421-5_3
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DOI: https://doi.org/10.1007/978-3-642-18421-5_3
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