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Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography
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1.
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, 154162 (2006).
http://dx.doi.org/10.1038/ncpcardio0482
2.
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, 12941306 (2011).
http://dx.doi.org/10.1118/1.3549761
3.
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), C22C26 (2008).
4.
4. P. Barlis and J. M. Schmitt, “Current and future developments in intracoronary optical coherence tomography imaging,” EuroIntervention 4, 529533 (2009).
http://dx.doi.org/10.4244/EIJV4I4A89
5.
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, 10351046 (2009).
http://dx.doi.org/10.1016/j.jcin.2009.06.019
6.
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, 176182 (2008).
http://dx.doi.org/10.1016/j.jcin.2008.01.007
7.
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, 11721181 (2010).
http://dx.doi.org/10.1007/s00270-010-0092-8
8.
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, 16401645 (2002).
http://dx.doi.org/10.1161/01.CIR.0000029927.92825.F6
9.
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, 15511555 (2005).
http://dx.doi.org/10.1161/01.CIR.0000159354.43778.69
10.
10. D. Stamper, N. J. Weissman, and M. Brezinski, “Plaque characterization with optical coherence tomography,” J. Am. Coll. Cardiol. 47, C6979 (2006).
http://dx.doi.org/10.1016/j.jacc.2005.10.067
11.
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, 24322440 (2001).
http://dx.doi.org/10.1118/1.1418726
12.
12. W. Drexler and J. G. Fujimoto, Optical Coherence Tomography (Springer, Berlin/Heidelberg, 2008).
13.
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, 30833098 (2008).
http://dx.doi.org/10.1088/0031-9155/53/12/001
14.
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, 15151519 (2006).
http://dx.doi.org/10.1253/circj.70.1515
15.
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, 371379 (2010).
http://dx.doi.org/10.4244/EIJV6I1A62
16.
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, 401415 (2010).
http://dx.doi.org/10.1093/eurheartj/ehp433
17.
17. S. Z. Li, Markov Random Field Modeling in Computer Vision (Springer-Verlag, New York, 2001).
18.
18. D. H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recogn. 13, 111122 (1981).
http://dx.doi.org/10.1016/0031-3203(81)90009-1
19.
19. D. Ioannou W. Huda, and A. F. Laine, “Circle recognition through a 2D Hough Transform and radius histogramming,” Image Vis. Comput. 17, 1526 (1999).
http://dx.doi.org/10.1016/S0262-8856(98)00090-0
20.
20. J. E. Besag, “Spatial interaction and the statistical analysis of lattice systems,” J. R. Stat. Soc. Ser. B 36, 192236 (1974).
21.
21. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE. Trans. Pattern. Anal. Mach. Intell. 6, 721741 (1984).
http://dx.doi.org/10.1109/TPAMI.1984.4767596
22.
22. S. Kirkpatrick, C. D. Gelatt, Jr., and M. P. Vecchi, “Optimization by simulated annealing,” Science 220, 671680 (1983).
http://dx.doi.org/10.1126/science.220.4598.671
23.
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, 19121919 (2005).
http://dx.doi.org/10.1109/TGRS.2005.848427
24.
24. M. A. Kupinski and M. L. Giger, “Automated seeded lesion segmentation on digital mammograms,” IEEE Trans. Med. Imaging 17, 510517 (1998).
http://dx.doi.org/10.1109/42.730396
25.
25. T. Lindeberg, Scale-Space Theory (Kluwer Academic Publishers, Boston, MA, 1997).
26.
26. S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 710732 (1992).
http://dx.doi.org/10.1109/34.142909
27.
27. R. Brinks, “On the convergence of derivatives of B-splines to derivatives of the Gaussian function,” Comput. Appl. Math. 27, 7992 (2008).
28.
28. T. Netsch and H. O. Peitgen, “Scale-space signatures for the detection of clustered microcalculations in digital mammograms,” IEEE. Trans. Med. Imaging 18, 774786 (1999).
http://dx.doi.org/10.1109/42.802755
29.
29. D. Specht, “Probabilistic neural networks,” Neural Networks 3, 109118 (1990).
http://dx.doi.org/10.1016/0893-6080(90)90049-Q
30.
30. T. Masters, Advanced Algorithms for Neural Networks (John Wiley & Sons, Academic, New York, 1995).
31.
31. E. Parzen, “On estimation of a probability density function and mode,” Ann. Math. Stat. 33, 10651076 (1962).
http://dx.doi.org/10.1214/aoms/1177704472
32.
32. S. Mallat and W. L. Hwand, “Singularity Detection And Processing With Wavelets,” IEEE Trans. Inf. Theory 38, 617643 (1992).
http://dx.doi.org/10.1109/18.119727
33.
33. N. R. Draper and H. Smith, Applied Regression Analysis (Wiley-Interscience, Hoboken, NJ, 1998).
34.
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, 404415 (2005).
http://dx.doi.org/10.1016/j.jbi.2005.02.008
35.
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, 228235 (2008).
http://dx.doi.org/10.1002/ccd.v72:2
36.
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. 589592 (2001).
37.
37. J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet 1, 307310 (1986).
http://dx.doi.org/10.1016/S0140-6736(86)90837-8
38.
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. 10891092 (2008).
39.
39. J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679698 (1986).
http://dx.doi.org/10.1109/TPAMI.1986.4767851
40.
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.
41. C. Kauffmann, P. Motreff, and L. Sarry, “in vivo supervised analysis of stent reendothelialization from optical coherence tomography,” IEEE Trans. Med. Imaging 29, 807818 (2010).
http://dx.doi.org/10.1109/TMI.2009.2037755
42.
42. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 6266 (1979).
http://dx.doi.org/10.1109/TSMC.1979.4310076
43.
43. G. Unal, S. Gurmeric, and S. G. Carlier, “Stent implant follow-up in intravascular optical coherence tomography images,” Int. J Cardiovasc. Imaging 26, 809816 (2010).
http://dx.doi.org/10.1007/s10554-009-9508-4
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/content/aapm/journal/medphys/39/1/10.1118/1.3673067
2011-12-30
2014-11-22

Abstract

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 OCTimages 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 OCTimages 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 OCTimages was designed and presented. The proposed algorithm may be employed for automated quantitative morphological analysis of in-stent neointimal hyperplasia.

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Scitation: Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/39/1/10.1118/1.3673067
10.1118/1.3673067
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