Analysis of Left Main Coronary Bifurcation Angle to Detect Stenosis
Narrowing of blood vessel due to plaque deposition is known as stenosis that acts as prime indicator of the coronary artery diseases (CAD). Coronary cine angiography (CCA) is a digital imaging modality used for the assessment of severity of coronary bifurcation lesions in the coronary arteries. Angiography based stenosis diagnosis is done as subjective analysis by the clinicians that results in overestimations or underestimations of detected stenosis. In stenosis, mostly the plaque deposition occurs at the left main coronary artery (LMCA) branch. Bifurcation angle at site of LMCA act as significant indicators of presences of stenosis. The proposed work involves segmentation of LMCA using various segmentation techniques such as Morphological based segmentation, Hessian detection and Active contour segmentation. Active contour segmentation provides clear visualization of LMCA structure when compare to all other segmentation methods. Then, computation of automatic bifurcation angle measurement at bifurcating regions of LMCA such as left anterior descending (LAD) and left circumflex (LCx) in both normal and stenotic images of CCA is performed. The diagnostic performance of stenosis yields a detection accuracy of 92%. The outcome of proposed work is found to be quantitative tool for the clinicians in accurate analysis of prediction of stenosis and also helpful during stent replacement surgical procedure in percutaneous coronary interventions.
KeywordsLeft main coronary artery Coronary Cine Angiogram Active contour segmentation Bifurcation angle Stenosis
We thank for the support of ‘Chettinad Hospital, Chennai’ for providing both normal and stenotic arteries of Coronary Cine Angiograms, which made us to do this project.
- 1.Cui, Y., Zeng, W., Yu, J., Lu, J., Hu, Y., Diao, N., Shi, H.: Quantification of left coronary bifurcation angles and plaques by coronary computed tomography angiography for prediction of significant coronary stenosis: a preliminary study with dual-source CT. PLoS One 12(3), e0174352 (2017)CrossRefGoogle Scholar
- 5.Fatemi, M.R., Mirhassani, S.M., Yousefi, B.: Vessel segmentation in X-ray angiographic images using Hessian based vesselness filter and wavelet based image fusion. In: 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB), pp. 1–5, November 2010Google Scholar
- 7.Mohan, N., Vishnukumar, S.: Detection and localization of coronary artery stenotic segments using image processing. In: International Conference on Emerging Technological Trends (ICETT), pp. 1–5, October 2016Google Scholar
- 8.Mahmood, N.H., Razif, M.R., Gany, M.T.: Comparison between median, unsharp and wiener filter and its effect on ultrasound stomach tissue image segmentation for pyloric stenosis. Int. J. Appl. Sci. Technol. 1(5) (2011)Google Scholar
- 9.Ersoy, I., Bunyak, F., Mackey, M.A., Palaniappan, K.: Cell segmentation using Hessian-based detection and contour evolution with directional derivatives. In: 15th IEEE International Conference on Image Processing ICIP, pp. 1804–1807, October 2008Google Scholar
- 10.Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Heidelberg, pp. 130–137, October 1998Google Scholar
- 13.Airouche, M., Bentabet, L., Zelmat, M.: Image segmentation using active contour model and level set method applied to detect oil spills. In: Proceedings of the World Congress on Engineering. Lecture Notes in Engineering and Computer Science, vol. 1, no. 1, pp. 1–3, July 2009Google Scholar
- 15.Khan, S.A., Hassan, A., Rashid, S.: Blood vessel segmentation and centerline extraction based on multilayered thresholding in CT images. In: The 2nd International Conference on Intelligent Systems and Image Processing (ICISIP 2014), September 2014Google Scholar
- 16.Bhuiyan, A., Nath, B., Ramamohanarao, K.: Detection and classification of bifurcation and branch points on retinal vascular network. In: International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 1–8, December 2012Google Scholar
- 17.Cao, Y., Liu, C., Jin, Q., Chen, Y., Yin, Q., Li, J., Zhao, W.: Automatic Bifurcation angle calculation in intravascular optical coherence tomography images. In: 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 650–654, June 2017Google Scholar