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Organ detection in medical images with discriminately trained deformable part model

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Organization
Abstract
Automatic organ segmentation on a full-body scan image is a challenging task as most of the organ segmentation methods require a prior knowledge about the position of the given organ within the image. In this paper we show, how discriminately trained deformable part model can be used to acquire this prior knowledge by constructing a multi-organ detection system based on it.
Keywords
FEATURES, DATABASE, SEGMENTATION, ANATOMICAL STRUCTURES, CHEST RADIOGRAPHS

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Citation

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MLA
Gál, Viktor, Etienne Kerre, and Domonkos Tikk. “Organ Detection in Medical Images with Discriminately Trained Deformable Part Model.” IEEE 9th International Conference on Computational Cybernetics (ICCC 2013). Ed. A Szakal. New York, NY, USA: IEEE, 2013. 153–157. Print.
APA
Gál, V., Kerre, E., & Tikk, D. (2013). Organ detection in medical images with discriminately trained deformable part model. In A. Szakal (Ed.), IEEE 9th International conference on Computational Cybernetics (ICCC 2013) (pp. 153–157). Presented at the IEEE 9th International conference on Computational Cybernetics (ICCC 2013), New York, NY, USA: IEEE.
Chicago author-date
Gál, Viktor, Etienne Kerre, and Domonkos Tikk. 2013. “Organ Detection in Medical Images with Discriminately Trained Deformable Part Model.” In IEEE 9th International Conference on Computational Cybernetics (ICCC 2013), ed. A Szakal, 153–157. New York, NY, USA: IEEE.
Chicago author-date (all authors)
Gál, Viktor, Etienne Kerre, and Domonkos Tikk. 2013. “Organ Detection in Medical Images with Discriminately Trained Deformable Part Model.” In IEEE 9th International Conference on Computational Cybernetics (ICCC 2013), ed. A Szakal, 153–157. New York, NY, USA: IEEE.
Vancouver
1.
Gál V, Kerre E, Tikk D. Organ detection in medical images with discriminately trained deformable part model. In: Szakal A, editor. IEEE 9th International conference on Computational Cybernetics (ICCC 2013). New York, NY, USA: IEEE; 2013. p. 153–7.
IEEE
[1]
V. Gál, E. Kerre, and D. Tikk, “Organ detection in medical images with discriminately trained deformable part model,” in IEEE 9th International conference on Computational Cybernetics (ICCC 2013), Tihany, Hungary, 2013, pp. 153–157.
@inproceedings{6861577,
  abstract     = {{Automatic organ segmentation on a full-body scan image is a challenging task as most of the organ segmentation methods require a prior knowledge about the position of the given organ within the image. In this paper we show, how discriminately trained deformable part model can be used to acquire this prior knowledge by constructing a multi-organ detection system based on it.}},
  author       = {{Gál, Viktor and Kerre, Etienne and Tikk, Domonkos}},
  booktitle    = {{IEEE 9th International conference on Computational Cybernetics (ICCC 2013)}},
  editor       = {{Szakal, A}},
  isbn         = {{9781479900602}},
  keywords     = {{FEATURES,DATABASE,SEGMENTATION,ANATOMICAL STRUCTURES,CHEST RADIOGRAPHS}},
  language     = {{eng}},
  location     = {{Tihany, Hungary}},
  pages        = {{153--157}},
  publisher    = {{IEEE}},
  title        = {{Organ detection in medical images with discriminately trained deformable part model}},
  url          = {{http://dx.doi.org/10.1109/ICCCyb.2013.6617579}},
  year         = {{2013}},
}

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