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Medical Physics / Volume 39 / Issue 1 / INFRARED AND MICROWAVE IMAGING

Med. Phys. 39, 503 (2012); http://dx.doi.org/10.1118/1.3673067 (11 pages)

Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography

Stavros Tsantis

Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 265 04, Greece and Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Egaleo GR-122 10, Athens, Greece

George C. Kagadis

Department of Medical Physics, School of Medicine, University of Patras,P.O. Box 132 73, Rion, GR 265 04, Greece

Konstantinos Katsanos and Dimitris Karnabatidis

Department of Radiology, School of Medicine, University of Patras, Rion, GR 265 04, Greece

George Bourantas and George C. Nikiforidis

Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 265 04, Greece

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(Received 21 June 2011; accepted 6 December 2011; revised 21 November 2011; published online 30 December 2011)

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Purpose: Optical coherence tomography (OCT) is a catheter-based imaging method that employs near-infrared light to produce high-resolution cross-sectional intravascular images. The authors propose a segmentation technique for automatic lumen area extraction and stent strut detection in intravascular OCT images for the purpose of quantitative analysis of neointimal hyperplasia (NIH).
Methods: A clinical dataset of frequency-domain OCT scans of the human femoral artery was analyzed. First, a segmentation method based on the Markov random field (MRF) model was employed for lumen area identification. Second, textural and edge information derived from local intensity distribution and continuous wavelet transform (CWT) analysis were integrated to extract the inner luminal contour. Finally, the stent strut positions were detected via the introduction of each strut wavelet response across scales into a feature extraction and classification scheme in order to optimize the strut position detection.
Results: The inner lumen contour and the position of stent strut were extracted with very high accuracy. Compared with manual segmentation by an expert vascular physician the automatic segmentation had an average overlap value of 0.937 ± 0.045 for all OCT images included in the study. The strut detection accuracy had an area under the curve (AUC) value of 0.95, together with sensitivity and specificity average values of 0.91 and 0.96, respectively.
Conclusions: A robust automatic segmentation technique integrating textural and edge information for vessel lumen border extraction and strut detection in intravascular OCT images was designed and presented. The proposed algorithm may be employed for automated quantitative morphological analysis of in-stent neointimal hyperplasia.

© 2012 American Association of Physicists in Medicine

Article Outline

  1. INTRODUCTION
  2. MATERIALS AND METHODS
    1. Materials
      1. OCT clinical dataset
    2. Lumen area identification
      1. Preprocessing
      2. Vessel lumen border detection
    3. Stent strut detection
    4. Clinical pilot application
  3. RESULTS
  4. DISCUSSION
  5. CONCLUSIONS
Digital Object Identifier
http://dx.doi.org/10.1118/1.3673067

KEYWORDS and PACS

Keywords

biomedical optical imaging, blood vessels, feature extraction, image classification, image segmentation, image texture, Markov processes, medical image processing, optical tomography, stents, wavelet transforms, optical coherence tomography, image segmentation, Markov random fields, continuous wavelet transform, image analysis, image quantification, neointimal hypersplasia

PACS

PUBLICATION DATA

ISSN

0094-2405 (print)  

Publisher

$abstract.society.value is a Member of CrossRef American Association of Physicists in Medicine

  1. A. F. Low, G. J. Tearney, B. E. Bouma, and I. K. Jang, “Technology Insight: Optical coherence tomography—Current status and future development,” Nat. Clin. Pract. Cardiovasc. Med. 3, 154–162 (2006).
  2. C. Zhang, M. C. Villa-Uriol, M. De Craene, J. M. Pozo, J. M. Macho, and A. F. Frangi, “Dynamic estimation of three-dimensional cerebrovascular deformation from rotational angiography,” Med. Phys. 38, 1294–1306 (2011)MPHYA6000038000003001294000001.
  3. P. Barlis, C. Di Mario, H. van Beusekom, N. Gonzalo, and E. Regar, “Novelties in cardiac imaging—Optical coherence tomography (OCT),” EuroIntervention 4(Suppl C), C22–C26 (2008).
  4. P. Barlis and J. M. Schmitt, “Current and future developments in intracoronary optical coherence tomography imaging,” EuroIntervention 4, 529–533 (2009).
  5. H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: A comprehensive review clinical and research applications,” JACC: Cardiovasc. Intervent. 2, 1035–1046 (2009).
  6. G. Pundziute, J. D. Schuijf, J. W. Jukema, I. Decramer, G. Sarno, P. K. Vanhoenacker, J. H. Reiber, M. J. Schalij, W. Wijns, and J. J. Bax, “Head-to-head comparison of coronary plaque evaluation between multislice computed tomography and intravascular ultrasound radiofrequency data analysis,” JACC: Cardiovasc. Intervent. 1, 176–182 (2008).
  7. D. Karnabatidis, K. Katsanos, I. Paraskevopoulos, A. Diamantopoulos, S. Spiliopoulos, and D. Siablis, “Frequency-domain intravascular optical coherence tomography of the femoropopliteal artery,” Cardiovasc. Intervent. Radiol. 34, 1172–1181 (2010).
  8. H. Yabushita, B. E. Bouma, S. L. Houser, H. T. Aretz, I. K. Jang, K. H. Schlendorf, C. R. Kauffman, M. Shishkov, D. H. Kang, E. F. Halpern, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002). [MEDLINE]
  9. I. K. Jang, G. J. Tearney, B. MacNeill, M. Takano, F. Moselewski, N. Iftima, M. Shishkov, S. Houser, H. T. Aretz, E. F. Halpern, and B. E. Bouma, “in vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography,” Circulation 111, 1551–1555 (2005). [MEDLINE]
  10. D. Stamper, N. J. Weissman, and M. Brezinski, “Plaque characterization with optical coherence tomography,” J. Am. Coll. Cardiol. 47, C69–79 (2006). [MEDLINE]
  11. Y. Pan, J. P. Lavelle, S. I. Bastacky, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Farkas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001)MPHYA6000028000012002432000001. [ISI] [MEDLINE]
  12. W. Drexler and J. G. Fujimoto, Optical Coherence Tomography (Springer, Berlin/Heidelberg, 2008).
  13. G. T. Bonnema, K. O. Cardinal, S. K. Williams, and J. K. Barton, “An automatic algorithm for detecting stent endothelialization from volumetric optical coherence tomography datasets,” Phys. Med. Biol. 53, 3083–3098 (2008).
  14. T. Sawada, J. Shite, T. Shinke, S. Watanabe, H. Otake, D. Matsumoto, Y. Imuro, D. Ogasawara, O. L. Paredes, and M. Yokoyama, “Persistent malapposition after implantation of sirolimus-eluting stent into intramural coronary hematoma: Optical coherence tomography observations,” Circ. J. 70, 1515–1519 (2006).
  15. T. Okamura, N. Gonzalo, J. L. Gutierrez-Chico, P. W. Serruys, N. Bruining, S. de Winter, J. Dijkstra, K. H. Commossaris, R. J. van Geuns, G. van Soest, J. Ligthart, and E. Regar, “Reproducibility of coronary Fourier domain optical coherence tomography: Quantitative analysis of in vivo stented coronary arteries using three different software packages,” EuroIntervention 6, 371–379 (2010).
  16. F. Prati, E. Regar, G. S. Mintz, E. Arbustini, C. Di Mario, I. K. Jang, T. Akasaka, M. Costa, G. Guagliumi, E. Grube, Y. Ozaki, F. Pinto, and P. W. Serruys, “Expert review document on methodology, terminology, and clinical applications of optical coherence tomography: Physical principles, ethodology of image acquisition, and clinical application for assessment of coronary arteries and atherosclerosis,” Eur. Heart J. 31, 401–415 (2010).
  17. S. Z. Li, Markov Random Field Modeling in Computer Vision (Springer-Verlag, New York, 2001).
  18. D. H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recogn. 13, 111–122 (1981).
  19. D. Ioannou W. Huda, and A. F. Laine, “Circle recognition through a 2D Hough Transform and radius histogramming,” Image Vis. Comput. 17, 15–26 (1999). [Inspec]
  20. J. E. Besag, “Spatial interaction and the statistical analysis of lattice systems,” J. R. Stat. Soc. Ser. B 36, 192–236 (1974).
  21. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE. Trans. Pattern. Anal. Mach. Intell. 6, 721–741 (1984). [Inspec] [ISI]
  22. S. Kirkpatrick, C. D. Gelatt, Jr., and M. P. Vecchi, “Optimization by simulated annealing,” Science 220, 671–680 (1983).
  23. T. N. Tran, R. Wehrens, D. H. Hoekman, and L. M. C. Buydens, “Initialization of Markov random field clustering of large remote sensing images,” IEEE Trans. Geosci. Remote Sens. 43, 1912–1919 (2005).
  24. M. A. Kupinski and M. L. Giger, “Automated seeded lesion segmentation on digital mammograms,” IEEE Trans. Med. Imaging 17, 510–517 (1998). [Inspec] [ISI] [MEDLINE]
  25. T. Lindeberg, Scale-Space Theory (Kluwer Academic Publishers, Boston, MA, 1997).
  26. S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 710–732 (1992).
  27. R. Brinks, “On the convergence of derivatives of B-splines to derivatives of the Gaussian function,” Comput. Appl. Math. 27, 79–92 (2008).
  28. T. Netsch and H. O. Peitgen, “Scale-space signatures for the detection of clustered microcalculations in digital mammograms,” IEEE. Trans. Med. Imaging 18, 774–786 (1999). [Inspec] [ISI] [MEDLINE]
  29. D. Specht, “Probabilistic neural networks,” Neural Networks 3, 109–118 (1990). [Inspec] [ISI]
  30. T. Masters, Advanced Algorithms for Neural Networks (John Wiley & Sons, Academic, New York, 1995).
  31. E. Parzen, “On estimation of a probability density function and mode,” Ann. Math. Stat. 33, 1065–1076 (1962). [ISI]
  32. S. Mallat and W. L. Hwand, “Singularity Detection And Processing With Wavelets,” IEEE Trans. Inf. Theory 38, 617–643 (1992). [Inspec] [ISI]
  33. N. R. Draper and H. Smith, Applied Regression Analysis (Wiley-Interscience, Hoboken, NJ, 1998).
  34. T. A. Lasko, J. G. Bhagwat, K. H. Zou, and L. Ohno-Machado, “The use of receiver operating characteristic curves in biomedical informatics,” J. Biomed. Inf. 38, 404–415 (2005). [MEDLINE]
  35. S. Tanimoto, G. Rodriguez-Granillo, P. Barlis, S. de Winter, N. Bruining, R. Hamers, M. Knappen, S. Verheye, P. W. Serruys, and E. Regar, “A novel approach for quantitative analysis of intracoronary optical coherence tomography: High inter-observer agreement with computer-assisted contour detection,” Cathet. Cardiovasc. Intervent. 72, 228–235 (2008).
  36. R. Hamers, N. Bruining, M. Knook, M. Sabate, and J. R. T. C. Roelandt, “A novel approach to quantitative analysis of intra vascular ultrasound images,” Comput. Cardiol. 589–592 (2001).
  37. J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet 1, 307–310 (1986). [MEDLINE]
  38. K. Sihan, C. Botka, F. Post, S. de Winter, E. Regar, R. Hamers, and N. Bruining, “A novel approach to quantitative analysis of intravascular optical coherence tomography imaging,” Comput. Cardiol. 1089–1092 (2008).
  39. J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986). [Inspec] [ISI]
  40. S. Gurmeric, G. Unal, S. Carlier, Y. Yang, and G. Slabaugh, “Automatic stent implant follow-up in intravascular optical coherence tomography images,” in MICCAI-CVII: The International Workshop on Computer Vision for Intravascular Imaging (2008).
  41. C. Kauffmann, P. Motreff, and L. Sarry, “in vivo supervised analysis of stent reendothelialization from optical coherence tomography,” IEEE Trans. Med. Imaging 29, 807–818 (2010).
  42. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979). [Inspec] [ISI]
  43. G. Unal, S. Gurmeric, and S. G. Carlier, “Stent implant follow-up in intravascular optical coherence tomography images,” Int. J Cardiovasc. Imaging 26, 809–816 (2010).

Figures (8) Tables (5)

Figures (click on thumbnails to view enlargements)

FIG.1
Schematic representation of the segmentation algorithm.

FIG.1 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.2
Typical OCT image. (a) White arrow points at the bright concentric rings. (b) OCT image after the removal of concentric rings.

FIG.2 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.3
OCT image with struts. Zoomed image part indicates the bright line segment.

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FIG.4
Strut wavelet response across scales, denoted by dots. The strut wavelet response is derived via a coarse to fine tracking of the maxima/minima detected on the last scale of decomposition. The scale where strut response reaches its peak is considered to be the strut scale signature.

FIG.4 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.5
Typical OCT frame graphical reproduction with the different calculated lengths and areas; DS, DL, DLLL, AS, and AL.

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FIG.6
(a) OCT image, (b) random initialization, (c) MRF model clusters (white concerning the area that presents with a high light reflectance and black concerning the areas in the image without intense light reflectance), and (d) vessel lumen border.

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FIG.7
ROC curve by the empirical method for the best features combinations from the feature subset for the PNN classifier (MV: Mean value, SD: Standard deviation, VAR: Variance, a: Lipschitz exponents, SCV: Signature coefficient value, SS: Signature scale).

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FIG.8
(a) White stars are pointing to the struts positions detected by the proposed algorithm, whereas the white circle point at the strut position misclassified. (b) Outer boundary represents the b-spline interpolation of all strut positions toward re-endothelialization estimation after the correction made with the GUI.

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Tables

Table I. FD-OCT scan parameters of the femoral artery.7

View Table
Table II. Features extracted from each wavelet transform response across scales.

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Table III. Scale-space feature subset after stepwise regression analysis.

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Table IV. ROC analysis results.

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Table V. Quantitative results. Values expressed as means (standard deviation).

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