Approaches to segment stent area from Intravascular Optical Coherence Tomography
Gaiarsa, Veronica Meyer; Cardenas, Diego; Furuie, Sérgio Shiguemi
Abstract
Introduction: Cardiovascular diseases (CVD) have been the focus of research in recent years due to its high mortality rate. It is estimated that 17.5 million people died of CVD in 2012, from which 7.4 million were due to coronary heart disease (CHD). In order to monitor CHD patients and avoid waste of specialists' time, this study proposes the development of a method that segments the area contained by stent struts from Frequency Domain Intravascular Optical Coherence Tomography (the latest technology to view vessels internally) of coronary arteries. Methods: The novelty of this study is to find areas comprised by stent struts using two optimal strategies that are robust even with false positives and false negatives detection of stent struts. The first one uses an ellipse fitting algorithm and the other uses a cylinder fitting algorithm. Results: Both strategies obtained similar accuracy results close to 98% of true positives, but the cylinder technique showed a run time of at least 50 times higher than the ellipse technique. Conclusion: The methods were executed on 443 images with different characteristics showing robustness and usefulness in the medical area.
Keywords
References
Bezdek JC, Ehrlich R, Full W. FCM: the fuzzy c-means clustering algorithm. Computers & Geosciences. 1984; 10(2):191-203. http://dx.doi.org/10.1016/0098-3004(84)90020-7.
Bezerra HG, Costa M, Guagliumi G, Rollins AM, Simon DI. Intracoronary optical coherence tomography: a comprehensive review clinical and research applications. JACC: Cardiovascular Interventions. 2009; 2(11):1035-46. PMid:19926041. http://dx.doi.org/10.1016/j.jcin.2009.06.019.
Cohen B, Hasselbring B. Coronary heart disease: a guide to diagnosis and treatment. 2nd ed. Omaha, Nebraska: Addicus Books; 2007.
Gaiarsa V, Cardenas D, Furuie S. Seleção semi-automática de área contida por stent em FD-IOCT utilizando imagens sequenciais. In: Anais do 7° Simpósio de Instrumentação e Imagens Médicas; 6° Simpósio de Processamento de Sinais da UNICAMP. 2015 Out 21-23; Campinas, São Paulo. Campinas: Editora Atual; 2015. p. 129-132.
Halir R, Flusser J. Numerically stable direct least squares fitting of ellipses. In: Proceedings of the 6th International Conference in Central Europe on Computer Graphics and Visualization. 1998 Feb 9-13; Plzen, Czech Republic. Plzen: Vaclav Skala Union Agency; 1998. v. 98. p. 125-32.
Huttenlocher DP, Klanderman G, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1993; 15(9):850-63. http://dx.doi.org/10.1109/34.232073.
Kapur JN, Sahoo PK, Wong AKC. A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision Graphics and Image Processing. 1985; 29(3):273-85. http://dx.doi.org/10.1016/0734-189X(85)90125-2.
Li SZ. Markov random field modeling in computer vision. Singapore: Springer Science & Business Media; 1995.
Mallat S, Hwang WL. Singularity detection and processing with wavelets. IEEE Transactions on Information Theory. 1992; 38(2):617-43. http://dx.doi.org/10.1109/18.119727.
Mandelias K, Tsantis S, Spiliopoulos S, Katsakiori PF, Karnabatidis D, Nikiforidis GC, Kagadis GC. Automatic quantitative analysis of in-stent restenosis using FD-OCT in vivo intra-arterial imaging. Medical Physics. 2013; 40(6):63101. PMid:23718609. http://dx.doi.org/10.1118/1.4803461.
Mittal S. Coronary heart disease in clinical practice. London: Springer Science & Business Media; 2005.
Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979; 9(1):62-6. http://dx.doi.org/10.1109/TSMC.1979.4310076.
Tsantis S, Kagadis GC, Katsanos K, Karnabatidis D, Bourantas G, Nikiforidis GC. Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography. Medical Physics. 2012; 39(1):503-13. PMid:22225321. http://dx.doi.org/10.1118/1.3673067.
Udupa JK, LeBlanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, Hirsch BE, Woodburn J. A framework for evaluating image segmentation algorithms. Computerized Medical Imaging and Graphics. 2006; 30(2):75-87. PMid:16584976. http://dx.doi.org/10.1016/j.compmedimag.2005.12.001.
Ughi GJ, Adriaenssens T, Onsea K, Kayaert P, Dubois C, Sinnaeve P, Coosemans M, Desmet W, D’hooge J. Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage. International Journal of Cardiac Imaging. 2012; 28(2):229-41. PMid:21347593. http://dx.doi.org/10.1007/s10554-011-9824-3.
Wang A, Eggermont J, Dekker N, Garcia-Garcia HM, Pawar R, Reiber JH, Dijkstra J. Automatic stent strut detection in intravascular optical coherence tomographic pullback runs. International Journal of Cardiac Imaging. 2013; 29(1):29-38. PMid:22618433. http://dx.doi.org/10.1007/s10554-012-0064-y.
Wang Z, Jenkins MW, Linderman GC, Bezerra HG, Fujino Y, Costa MA, Wilson D, Rollins A. 3-D stent detection in intravascular OCT using a Bayesian network and graph search. IEEE Transactions on Medical Imaging. 2015; 34(7):1549-61. PMid:25751863. http://dx.doi.org/10.1109/TMI.2015.2405341.
World Health Organization. Global status report on noncommunicable diseases 2014 [Internet]. Genebra: WHO; 2014. [cited 2016 May 12]. Available from: http://goo.gl/xCcayL
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