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A Diffeomorphic Demons Approach to Statistical Shape Modeling

Journal Article


Abstract


  • Automatic segmentation of organs from medical images is indispensable for the applications of computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). Statistical Shape Models (SSMs) based scheme have been proved as the accurate and robust methods for extraction of anatomical structures. A key step of this approach is the need to place the sampled points(landmarks) with correspondence across the training set. On the one hand, the correspondence of landmarks is related the quality of SSMs. On the other hand, in many cases the location of key landmarks should be manipulated by physicians, since an unattended system is hard to use in most clinical applications. In this paper, we establish a dense correspondence across the whole training set automatically by surface features, which are registered using diffeomorphic demons approach. And the optimization is executed on spherical domain. We establish the SSM for lung regions, the deformation of where is greatly. Finally, we derive quantitative measures of model quality and comparison of segmentation results using the model with non-optimized correspondence.

Authors


  •   Li, Guangxu (external author)
  •   Wu, Jiaqi (external author)
  •   Xiao, Zhitao (external author)
  •   Lu, Huimin (external author)
  •   Kim, Hyoung (external author)
  •   Ogunbona, Philip O.

Publication Date


  • 2017

Citation


  • Li, G., Wu, J., Xiao, Z., Lu, H., Kim, H. & Ogunbona, P. (2017). A Diffeomorphic Demons Approach to Statistical Shape Modeling. Optics and Laser Technology, Online first 123-131. Studies in Computational Intelligence

Scopus Eid


  • 2-s2.0-85033680194

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1248

Number Of Pages


  • 8

Start Page


  • 123

End Page


  • 131

Volume


  • Online first

Place Of Publication


  • United Kingdom

Abstract


  • Automatic segmentation of organs from medical images is indispensable for the applications of computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). Statistical Shape Models (SSMs) based scheme have been proved as the accurate and robust methods for extraction of anatomical structures. A key step of this approach is the need to place the sampled points(landmarks) with correspondence across the training set. On the one hand, the correspondence of landmarks is related the quality of SSMs. On the other hand, in many cases the location of key landmarks should be manipulated by physicians, since an unattended system is hard to use in most clinical applications. In this paper, we establish a dense correspondence across the whole training set automatically by surface features, which are registered using diffeomorphic demons approach. And the optimization is executed on spherical domain. We establish the SSM for lung regions, the deformation of where is greatly. Finally, we derive quantitative measures of model quality and comparison of segmentation results using the model with non-optimized correspondence.

Authors


  •   Li, Guangxu (external author)
  •   Wu, Jiaqi (external author)
  •   Xiao, Zhitao (external author)
  •   Lu, Huimin (external author)
  •   Kim, Hyoung (external author)
  •   Ogunbona, Philip O.

Publication Date


  • 2017

Citation


  • Li, G., Wu, J., Xiao, Z., Lu, H., Kim, H. & Ogunbona, P. (2017). A Diffeomorphic Demons Approach to Statistical Shape Modeling. Optics and Laser Technology, Online first 123-131. Studies in Computational Intelligence

Scopus Eid


  • 2-s2.0-85033680194

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1248

Number Of Pages


  • 8

Start Page


  • 123

End Page


  • 131

Volume


  • Online first

Place Of Publication


  • United Kingdom