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K-core graph-based retinal vascular registration

Conference Paper


Abstract


  • Observing the differences of the blood capillary at different times is an effective method for contributing to the early diagnosis and treatment by differentiating the images. Therefore, distinguishing the differences of two retinal vascular images by registration should be regarded as the important precondition. We propose the method for solving the problem better than before. First a graph model of the vascular network is generated from the fundus image or other modality. Second, the graph is decomposed into a k-core representation which should transform a dense graph into a sparse version. Finally, the key nodes which kept high k-core value are remained for images registration by means of Iterative Closest Point algorithm. We gain the result that the average misalignment of k-core algorithm registration is infinitely close to zero, and the k-core algorithm is better than the SIFT algorithm clearly.

Authors


  •   Ruan, Mingzhe (external author)
  •   Ren, Xingxing (external author)
  •   Li, Guangxu (external author)
  •   Ogunbona, Philip O.
  •   Wu, Jun (external author)

Publication Date


  • 2018

Citation


  • Ruan, M., Ren, X., Li, G., Ogunbona, P. O. & Wu, J. (2018). K-core graph-based retinal vascular registration. ICBEB 2018 Proceedings of the 2nd International Conference on Biomedical Engineering and Bioinformatics (pp. 70-73). New York, United States: Association for Computing Machinery.

Scopus Eid


  • 2-s2.0-85060005013

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2323

Start Page


  • 70

End Page


  • 73

Place Of Publication


  • New York, United States

Abstract


  • Observing the differences of the blood capillary at different times is an effective method for contributing to the early diagnosis and treatment by differentiating the images. Therefore, distinguishing the differences of two retinal vascular images by registration should be regarded as the important precondition. We propose the method for solving the problem better than before. First a graph model of the vascular network is generated from the fundus image or other modality. Second, the graph is decomposed into a k-core representation which should transform a dense graph into a sparse version. Finally, the key nodes which kept high k-core value are remained for images registration by means of Iterative Closest Point algorithm. We gain the result that the average misalignment of k-core algorithm registration is infinitely close to zero, and the k-core algorithm is better than the SIFT algorithm clearly.

Authors


  •   Ruan, Mingzhe (external author)
  •   Ren, Xingxing (external author)
  •   Li, Guangxu (external author)
  •   Ogunbona, Philip O.
  •   Wu, Jun (external author)

Publication Date


  • 2018

Citation


  • Ruan, M., Ren, X., Li, G., Ogunbona, P. O. & Wu, J. (2018). K-core graph-based retinal vascular registration. ICBEB 2018 Proceedings of the 2nd International Conference on Biomedical Engineering and Bioinformatics (pp. 70-73). New York, United States: Association for Computing Machinery.

Scopus Eid


  • 2-s2.0-85060005013

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2323

Start Page


  • 70

End Page


  • 73

Place Of Publication


  • New York, United States