Robust anatomical correspondence detection by graph matching with sparsity constraint

Yanrong Guo, Guorong Wu, Yakang Dai, Jianguo Jiang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Graph matching is a robust correspondence detection approach which considers potential correspondences as graph nodes and uses graph links to measure the pairwise agreement between potential correspondences. In this paper, we propose a novel graph matching method to augment its power in establishing anatomical correspondences in medical images, especially for the cases with large inter-subject variations. Our contributions have twofold. First, we propose a robust measurement to characterize the pairwise agreement of appearance information on each graph link. In this way, our method is more robust to ambiguous matches than the conventional graph matching methods that generally consider only the simple geometric information. Second, although multiple correspondences are allowed for robust correspondence, we further introduce the sparsity constraint upon the possibilities of correspondences to suppress the distraction from misleading matches, which is very important for achieving accurate one-to-one correspondences in the end of the matching procedure. We finally incorporate these two improvements into a new objective function and solve it by quadratic programming. The proposed graph matching method has been evaluated in the public hand X-ray images with comparison to a conventional graph matching method. In all experiments, our method achieves the best matching performance in terms of matching accuracy and robustness.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages20-28
Number of pages9
Volume7766 LNCS
DOIs
Publication statusPublished - 2013 Mar 25
Externally publishedYes
Event2nd MICCAI Workshop on Medical Computer Vision, MICCAI-MCV 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 2012 Oct 52012 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7766 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd MICCAI Workshop on Medical Computer Vision, MICCAI-MCV 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period12/10/512/10/5

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Guo, Y., Wu, G., Dai, Y., Jiang, J., & Shen, D. (2013). Robust anatomical correspondence detection by graph matching with sparsity constraint. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7766 LNCS, pp. 20-28). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7766 LNCS). https://doi.org/10.1007/978-3-642-36620-8_3