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Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 1–16Cite as

Accurate Vessel Segmentation with Progressive Contrast Enhancement and Canny Refinement

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

Abstract

Vessel segmentation is a key step for various medical applications, such as diagnosis assistance, quantification of vascular pathology, and treatment planning. This paper describes an automatic vessel segmentation framework which can achieve highly accurate segmentation even in regions of low contrast and signal-to-noise-ratios (SNRs) and at vessel boundaries with disturbance induced by adjacent non-vessel pixels. There are two key contributions of our framework. The first is a progressive contrast enhancement method which adaptively improves contrast of challenging pixels that were otherwise indistinguishable, and suppresses noises by weighting pixels according to their likelihood to be vessel pixels. The second contribution is a method called canny refinement which is based on a canny edge detection algorithm to effectively re-move false positives around boundaries of vessels. Experimental results on a public retinal dataset and our clinical cerebral data demonstrate that our approach outperforms state-of-the-art methods including the vesselness based method [1] and the optimally oriented flux (OOF) based method [2].

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References

  1. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  2. Law, M., Chung, A.: Three dimensional curvilinear structure detection using op-timally oriented flux. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) Computer Vision–ECCV 2008. LNCS, vol. 5305, pp. 368–382. Springer, Berlin (2008)

    Chapter  Google Scholar 

  3. Agam, G., Wu, C.: Probabilistic modeling-based vessel enhancement in thoracic CT scans. In: Proceedings of CVPR (2005)

    Google Scholar 

  4. Santamaría-Pang, A., Colbert, C.M., Saggau, P., Kakadiaris, I.: Automatic center-line extraction of irregular tubular structures using probability volumes from mul-tiphoton imaging. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. LNCS, vol. 4792, pp. 486–494. Springer, Berlin (2007)

    Chapter  Google Scholar 

  5. Rigamonti, R., Lepetit, V.: Accurate and efficient linear structure segmentation by leveraging ad hoc deatures with learned filters. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. LNCS, vol. 7510, pp. 189–197. Springer, Berlin (2012)

    Chapter  Google Scholar 

  6. Becker, C., Rigamonti, R., Lepetit, V., Fua, P.: Supervised feature learning for curvilinear structure segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 526–533. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  7. Rigamonti, R., Sironi, A., Lepetit, V., Fua, P.: Learning separable filters. In: Proceedings of CVPR (2013)

    Google Scholar 

  8. Gonzalez, G., Fleuret, F., Fua, P.: Learning rotational features for filament detec-tion. In: Proceedings of CVPR (2009)

    Google Scholar 

  9. Yang, X., Cheng, K.-T., Chien, A.-C.: Geodesic Active Contour with Adaptive Configuration for Cerebral Vessel and Aneurysm Segmentation. In: Proceedings of ICPR (2014)

    Google Scholar 

  10. Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D Active Contour Segmentation of Anatomical Structures: Significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)

    Article  Google Scholar 

  11. Nain, D., Yezzi, A.J., Turk, G.: Vessel segmentation using a shape driven flow. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 51–59. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. Int. J. Comput. Vis. 22, 61–79 (1997)

    Article  MATH  Google Scholar 

  13. Zhu, S.C., Yullie, A.: Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18(9), 884–900 (1996)

    Article  Google Scholar 

  14. Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A Review of 3D Vessel Lumen Segmentation Techniques: Models, Features and Extraction Schemes. Med. Image Anal. 13, 819–845 (2009)

    Article  Google Scholar 

  15. Zuiderveld, K.: Contrast limit adaptive histogram equalization. Graphics Gems IV, pp. 474–485. Academic Press, Boston (1994)

    Chapter  Google Scholar 

  16. Kirbas, C., Quek, F.: A Review of Vessel Extraction Techniques and Algorithms. J. ACM Comput. Surv. 36(2), 81–121 (2004)

    Article  Google Scholar 

  17. Optimally Oriented Flux implementation. https://github.com/fethallah/ITK-TubularGeodesics

  18. Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based Vessel Segmentation in Color Images of the Retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)

    Article  Google Scholar 

  19. Freeman, W.T., Adelson, E.H.: The Design and Use of Steerable Filters. IEEE Trans. Pattern Anal. Mach. Intell. 13, 891–906 (1991)

    Article  Google Scholar 

  20. Shikata, H., Hoffman, E.A., Sonka, M.: Automated Segmentation of Pulmonary Vascular Tree from 3D CT Images. Proc. SPIE Med. Imaging 5369, 107–116 (2004)

    Article  Google Scholar 

  21. Lin, Q.: Enhancement, Detection, and Visualization of 3D Volume Data. Ph.D. Thesis, Dept. EE, Linkoping University, SE-581 83 Linkoping, Sweden, Dissertations No. 824, May 2003

    Google Scholar 

  22. Armande, N., Montesinos, P., Monga, O.: A 3D thin nets extraction method for medical imaging. In Proceedings of ICPR, p. 642 (1996)

    Google Scholar 

  23. Prinet, V., Monaga, O., Ge, C., Xie, S.L., Ma, S.D.: Thin network extraction in 3D images: application to medical angiograms. In Proceedings of ICPR, pp. 386–390 (1996)

    Google Scholar 

  24. Bauer, C., Bischof, H.: A novel approach for detection of tubular objects and its application to medical image analysis. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 163–172. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  25. Canny, J.: A Computational Approach To Edge Detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

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Acknowledgement

This work was supported in part by the Society of Interventional Radiology (SIR) Foundation Dr. Ernest J. Ring Academic Development Grant, The Aneurysm and AVM Foundation (TAAF) Cerebrovascular Research Grant, and a UCLA Radiology Exploratory Research Grant.

Author information

Authors and Affiliations

  1. Department of Electronics Information Engineering, HUST, Wuhan, China

    Xin Yang

  2. Department of Electrical Computer Engineering, UCSB, Santa Barbara, CA, USA

    Xin Yang & Kwang-Ting Tim Cheng

  3. Division of Interventional Neuroradiology, UCLA, Medical School, Los Angeles, CA, USA

    Aichi Chien

Authors
  1. Xin Yang
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  2. Kwang-Ting Tim Cheng
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  3. Aichi Chien
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Corresponding author

Correspondence to Xin Yang .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Yang, X., Cheng, KT., Chien, A. (2015). Accurate Vessel Segmentation with Progressive Contrast Enhancement and Canny Refinement. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-16811-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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