Abstract
Angiography is the gold standard for diagnosis and interventional treatment of vascular pathologies, especially for stenosis, in many hospitals around the world. Still, the physicians complain of visual burdens due to its rather low spatial resolution and artefacts. Fluoroscopic angiography series from the dataset are obtained with standard clinical protocol. In the following, there is proposed an algorithm for vessel segmentation and edge detection. It is related to gradient operator applied on a pre-processed image with the Frangi’s vesselness filtering for removing the equipment acquisition noises, followed by morphological operations for removing the spurs and adaptive threholding. The contour tracking along the vessel is done using Dijkstra’s smallest path algorithm. The severity of the stenosis for a vessel segment can be assessed visually by a medical imagistic expert. The angiograph software can provide a graphic containing on the X axis the vessel segment length and on Y axis its corresponding cross-sectional areas. More objectively, the percentage of area stenosis can be computed.
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References
Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36(2), 81–121 (2004)
Dehkordi, M.T., Sadri, S., Doosthoseini, A.: A review of coronary vessel segmentation algorithms. J. Med. Signals Sens. 1(1), 49–54 (2011)
Wan, T., Shang, X., Yang, W., Chen, J., Li, D., Qin, Z.: Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging. Comput. Methods Programs Biomed. 157, 179–190 (2018)
Wiesel, J., Grunwald, A.M., Tobiasz, C., Robin, B., Bodenheimer, M.M.: Quantitation of absolute area of a coronary arterial stenosis: experimental validation with a preparation in vivo. Circulation 74(5), 1099–1106 (1986)
Zhou, J., et al.: Quantification of coronary artery Stenosis by Area Stenosis from cardiac CT angiography. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, pp. 695–698 (2015)
Scherl, H., et al.: Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3D CTA data sets. Med. Image Anal. 11(1), 21–34 (2006)
Molloi, S., Johnson, T., Ding, H., Lipinski, J.: Accurate quantification of vessel cross-sectional area using CT angiography: a simulation study. Int. J. Cardiovasc. Imaging 33(3), 411–419 (2016)
Kirişli, H.A., et al.: Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med. Image Anal. 17(8), 859–876 (2013)
Sarry, L., et al.: Assessment of stenosis severity using a novel method to estimate spatial and temporal variations of blood flow velocity in biplane coronarography. Phys. Med. Biol. 42, 1549 (1997)
Bonnefous, O., Pereira, V.M., Ouared, R., Brina, O., Aerts, H., Hermans, R., van Nijnatten, F., Stawiaski, J., Ruijters, D.: Quantification of arterial flow using digital subtraction angiography. Med. Phys. 39(10), 6264–6275 (2012)
Ishibashi, Y., Grundeken, M.J., Nakatami, S.: In vitro validation and comparison of different software packages or algorithms for coronary bifurcation analysis using calibrated phantoms: implications for clinical practice and research of bifurcation stenting. Catheter. Cardiovasc. Interv. 85, 554–563 (2014)
Zhou, H., Wu, J., Zhang, J.: Digital Image Processing: Part I. Ventus Publishing ApS (2010)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Deriche, R.: Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int. J. Comput. Vis. 1, 167–187 (1987)
Kumar, S.S., Amutha, R.: Edge detection of angiogram images using the classical image processing techniques. In: IEEE-International Conference on Advances In Engineering, Science and Management (ICAESM 2012), pp. 55–60 (2012)
Reiber, J.H.C.: Introduction to QCA, IVUS and OCT in interventional cardiology. Int. J. Cardiovasc. Imaging 27(2), 153–154 (2011)
Acknowledgment
The work has been funded by the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125.
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Tache, I.A., Glotsos, D. (2020). Vessel Segmentation and Stenosis Quantification from Coronary X-Ray Angiograms. In: Su, R., Liu, H. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2020. Lecture Notes in Electrical Engineering, vol 633. Springer, Singapore. https://doi.org/10.1007/978-981-15-5199-4_4
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DOI: https://doi.org/10.1007/978-981-15-5199-4_4
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