Advertisement

Medical & Biological Engineering & Computing

, Volume 56, Issue 9, pp 1515–1530 | Cite as

Vessel segmentation and catheter detection in X-ray angiograms using superpixels

  • Hamid R. FazlaliEmail author
  • Nader Karimi
  • S. M. Reza Soroushmehr
  • Shahram Shirani
  • Brahmajee K. Nallamothu
  • Kevin R. Ward
  • Shadrokh Samavi
  • Kayvan Najarian
Original Article
  • 355 Downloads

Abstract

Coronary artery disease (CAD) is the leading cause of death around the world. One of the most common imaging methods for diagnosing CAD is the X-ray angiography (XRA). Diagnosing using XRA images is usually challenging due to some reasons such as, non-uniform illumination, low contrast, presence of other body tissues, and presence of catheter. These challenges make the diagnosis task hard and more prone to misdiagnosis. In this paper, we propose a new method for coronary artery segmentation, catheter detection, and centerline extraction in X-ray angiography images. For the segmentation, initially, three different superpixel scales are exploited, and a measure for vesselness probability of each superpixel is determined. A voting mechanism is used for obtaining an initial segmentation map from the three superpixel scales. The initial segmentation is refined by finding the orthogonal line on each ridge pixel of vessel region. The catheter is detected in the first frame of the angiography sequence and is tracked in other frames by fitting a second order polynomial on it. Also, we use the image ridges for extracting the coronary artery centerlines. We evaluated and compared our method with one of the previous well-known coronary artery segmentation methods on two challenging datasets. The results show that our method can segment the vessels and also detect and track the catheter in the XRA sequences. In general, the results assessed by a cardiologist show that 83% of the images processed by our proposed segmentation method were labeled as good or excellent, while this score for the compared method is 48%. Also, the evaluation results show that our method performs 67% faster than the compared method.

Graphical abstract

Proposed framework for coronary artery detection

Keywords

Coronary artery segmentation Catheter detection Centerline extraction Superpixel X-ray angiogram 

Notes

Acknowledgments

The authors would like to thank Sina Heart Center, Isfahan, Iran for providing us with angiogram videos and also Dr. Antonio Hernández Vela from Universitat de Barcelona for sharing with us the source codes of vessel segmentation [11].

References

  1. 1.
    Kato S, Kitagawa K, Ishida N, Ishida M, Nagata M, Ichikawa Y, Katahira K, Matsumoto Y, Seo K, Ochiai R, Kobayashi Y, Sakuma H (2010) Assessment of coronary artery disease using magnetic resonance coronary angiography: a national multicenter trial. J Am Coll Cardiol 56(12):983–991.  https://doi.org/10.1016/j.jacc.2010.01.071 CrossRefPubMedGoogle Scholar
  2. 2.
    Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Medical image computing and computer-assisted intervention, pp 130–137Google Scholar
  3. 3.
    Truc PT, Khan MA, Lee YK, Lee S, Kim TS (2009) Vessel enhancement filter using directional filter bank. Comput Vis Image Underst 113(1):101–112.  https://doi.org/10.1016/j.cviu.2008.07.009 CrossRefGoogle Scholar
  4. 4.
    Fazlali HR, Karimi N, Soroushmehr SMR, Sinha S, Samavi BN, Najarian K (2015) Vessel region detection in coronary X-ray angiograms. In International conference on image processing (pp. 1493–1497)Google Scholar
  5. 5.
    Petkov S, Carrillo X, Radeva P, Gatta C (2014) Diaphragm border detection in coronary X-ray angiographies: new method and applications. Comput Med Imaging Graph 38(4):296–305.  https://doi.org/10.1016/j.compmedimag.2014.01.003 CrossRefPubMedGoogle Scholar
  6. 6.
    Ma H, Hoogendoorn A, Regar E, Niessen WJ, van Walsum T (2017) Automatic online layer separation for vessel enhancement in X-ray angiograms for percutaneous coronary interventions. Med Image Anal 39:145–161.  https://doi.org/10.1016/j.media.2017.04.011 CrossRefPubMedGoogle Scholar
  7. 7.
    Tang S, Wang Y, Chen YW (2012) Application of ICA to X-ray coronary digital subtraction angiography. Neurocomputing 79:168–172.  https://doi.org/10.1016/j.neucom.2011.10.012 CrossRefGoogle Scholar
  8. 8.
    Nejati M, Pourghassem H (2014) Multiresolution image registration in digital X-ray angiography with intensity variation modeling. J Med Syst 38(2):1–10CrossRefGoogle Scholar
  9. 9.
    Kirbas C, Quek FK (2003) Vessel extraction techniques and algorithms: a survey. In IEEE Symposium on Bioinformatics and Bioengineering (pp. 238–245)Google Scholar
  10. 10.
    Felfelian B, Fazlali HR, Karimi N, Soroushmehr SMR, Samavi S, Nallamothu B, Najarian K (2017) Vessel segmentation in low contrast X-ray angiogram images. International Conference on Image Processing (ICIP), pp. 375–379Google Scholar
  11. 11.
    Hernández-Vela A, Gatta C, Escalera S, Igual L, Martin-Yuste V, Sabaté M, Radeva P (2012) Accurate coronary centerline extraction, caliber estimation, and catheter detection in angiographies. IEEE Trans Inf Technol Biomed 16(6):1332–1340.  https://doi.org/10.1109/TITB.2012.2220781 CrossRefPubMedGoogle Scholar
  12. 12.
    Fazlali HR, Karimi N, Soroushmehr SMR, Samavi S, Nallamothu B, Derksen H, Najarian K (2015) Robust catheter identification and tracking in X-ray angiographic sequences. In Engineering in medicine and biology conference (pp. 7901–7904)Google Scholar
  13. 13.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282.  https://doi.org/10.1109/TPAMI.2012.120 CrossRefPubMedGoogle Scholar
  14. 14.
    Bai X, Zhou F, Xue B (2012) Image enhancement using multi scale image features extracted by top-hat transform. Opt Laser Technol 44(2):328–336.  https://doi.org/10.1016/j.optlastec.2011.07.009 CrossRefGoogle Scholar
  15. 15.
    He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409.  https://doi.org/10.1109/TPAMI.2012.213 CrossRefPubMedGoogle Scholar
  16. 16.
    López AM, Lumbreras F, Serrat J, Villanueva JJ (1999) Evaluation of methods for ridge and valley detection. IEEE Trans Pattern Anal Mach Intell 21(4):327–335.  https://doi.org/10.1109/34.761263 CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringMcMaster UniversityHamiltonCanada
  2. 2.Department of Electrical and Computer EngineeringIsfahan University of TechnologyIsfahanIran
  3. 3.Michigan Center for Integrative Research in Critical CareUniversity of MichiganAnn ArborUSA
  4. 4.Department of Emergency MedicineUniversity of MichiganAnn ArborUSA
  5. 5.Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborUSA

Personalised recommendations