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Learning-based prostate localization for image guided radiation therapy

Conference Paper


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Abstract


  • Accurate prostate localization is the key to the success of radiotherapy. It remains a difficult problem for CT images due to the low image contrast, the prostate motion, and the uncertain presence of rectum gas. In this paper, a learning based framework is proposed to improve the accuracy of prostate detection in CT. It adaptively determines distinctive feature types at distinctive image regions, thus filtering out features that are salient in image appearance, but irrelevant to prostate localization. Furthermore, an image similarity function is learned to make the image appearance distance consistent with the underlying prostate alignment. The efficacy of our proposed method has been demonstrated by the experiment.

Authors


  •   Zhou, Luping
  •   Liao, Shu (external author)
  •   Li, Wei (external author)
  •   Shen, Dinggang (external author)

Publication Date


  • 2011

Citation


  • Zhou, L., Liao, S., Li, W. & Shen, D. (2011). Learning-based prostate localization for image guided radiation therapy. 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 2103-2106). Chicago, United States: Institute of Electrical and Electronics Engineers.

Scopus Eid


  • 2-s2.0-80053495849

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2784&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1775

Has Global Citation Frequency


Start Page


  • 2103

End Page


  • 2106

Place Of Publication


  • Chicago, United States

Abstract


  • Accurate prostate localization is the key to the success of radiotherapy. It remains a difficult problem for CT images due to the low image contrast, the prostate motion, and the uncertain presence of rectum gas. In this paper, a learning based framework is proposed to improve the accuracy of prostate detection in CT. It adaptively determines distinctive feature types at distinctive image regions, thus filtering out features that are salient in image appearance, but irrelevant to prostate localization. Furthermore, an image similarity function is learned to make the image appearance distance consistent with the underlying prostate alignment. The efficacy of our proposed method has been demonstrated by the experiment.

Authors


  •   Zhou, Luping
  •   Liao, Shu (external author)
  •   Li, Wei (external author)
  •   Shen, Dinggang (external author)

Publication Date


  • 2011

Citation


  • Zhou, L., Liao, S., Li, W. & Shen, D. (2011). Learning-based prostate localization for image guided radiation therapy. 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 2103-2106). Chicago, United States: Institute of Electrical and Electronics Engineers.

Scopus Eid


  • 2-s2.0-80053495849

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2784&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1775

Has Global Citation Frequency


Start Page


  • 2103

End Page


  • 2106

Place Of Publication


  • Chicago, United States