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BioMed Research International
Volume 2016, Article ID 3530251, 7 pages
http://dx.doi.org/10.1155/2016/3530251
Research Article

Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method

1College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
2Department of Radiology, Navy General Hospital, Beijing 100048, China
3Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing 100142, China

Received 17 July 2016; Accepted 26 September 2016

Academic Editor: Yong Xia

Copyright © 2016 Yun Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. J. Bache, “Coronary artery disease: regulation of coronary blood flow,” in Coronary Artery Disease, pp. 57–67, Springer, Berlin, Germany, 2015. View at Google Scholar
  2. C. Zhou, H.-P. Chan, A. Chughtai et al., “Automated coronary artery tree extraction in coronary CT angiography using a multiscale enhancement and dynamic balloon tracking (MSCAR-DBT) method,” Computerized Medical Imaging and Graphics, vol. 36, no. 1, pp. 1–10, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Shahzad, H. Kirişli, C. Metz et al., “Automatic segmentation, detection and quantification of coronary artery stenoses on CTA,” International Journal of Cardiovascular Imaging, vol. 29, no. 8, pp. 1847–1859, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. İ. Öksüz, D. Ünay, and K. Kadıpaşaoğlu, “A hybrid method for coronary artery stenoses detection and quantification in CTA images,” in Proceedings of the MICCAI Workshop 3D Cardiovascular Imaging: A MICCAI Segmentation, 2012.
  5. Y. Kitamura, Y. Li, W. Ito, and H. Ishikawa, “Coronary lumen and plaque segmentation from CTA using higher-order shape prior,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2014, P. Golland, N. Hata, C. Barillot, J. Hornegger, and R. Howe, Eds., vol. 8673 of Lecture Notes in Computer Science, pp. 339–347, Springer, Berlin, Germany, 2014. View at Publisher · View at Google Scholar
  6. F. Lugauer, Y. Zheng, J. Hornegger, and B. M. Kelm, “Precise lumen segmentation in coronary computed tomography angiography,” Medical Computer Vision: Algorithms for Big Data, vol. 8848, pp. 137–147, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Zheng, M. Loziczonek, B. Georgescu, S. K. Zhou, F. Vega-Higuera, and D. Comaniciu, “Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes,” in Proceedings of the Medical Imaging 2011: Image Processing, vol. 7962, 2011.
  8. C. Zhou, H.-P. Chan, A. Chughtai et al., “Automatic identification of origins of left and right coronary arteries in CT angiography for coronary arterial tree tracking and plaque detection,” in Proceedings of the Medical Imaging 2013: Computer-Aided Diagnosis, vol. 8670 of Proceedings of SPIE, Medical Imaging 2013, Lake Buena Vista, Fla, USA, February 2013. View at Publisher · View at Google Scholar
  9. B. Bouraoui, C. Ronse, J. Baruthio, N. Passat, and P. Germain, “3D segmentation of coronary arteries based on advanced mathematical morphology techniques,” Computerized Medical Imaging and Graphics, vol. 34, no. 5, pp. 377–387, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” in Medical Image Computing and Computer-Assisted Interventation—MICCAI '98, W. M. Wells, A. Colchester, and S. L. Delp, Eds., vol. 1496 of Lecture Notes in Computer Science, pp. 130–137, Springer, Berlin, Germany, 1998. View at Google Scholar
  11. H. A. Kirisli, M. Schaap, S. Klein et al., “Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach,” in Proceedings of the Medical Imaging 2010: Image Processing, B. M. Dawant and D. R. Haynor, Eds., Proceedings of SPIE, San Diego, Calif, USA, February 2010. View at Publisher · View at Google Scholar
  12. Y. Sato, S. Nakajima, N. Shiraga et al., “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Medical Image Analysis, vol. 2, no. 2, pp. 143–168, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Motoyama, H. Ito, M. Sarai et al., “Plaque characterization by coronary computed tomography angiography and the likelihood of acute coronary events in mid-term follow-up,” Journal of the American College of Cardiology, vol. 66, no. 4, pp. 337–346, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. H. A. Kirişli, M. Schaap, C. T. Metz et al., “Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography,” Medical Image Analysis, vol. 17, no. 8, pp. 859–876, 2013. View at Publisher · View at Google Scholar · View at Scopus