Advanced Computational Method to Extract Heart Artery Region
Advanced Computational Method to Extract Heart Artery Region
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : K. Kavipriya, Manjunatha Hiremath
|DOI : 10.14445/22315381/IJETT-V70I6P237|
How to Cite?
K. Kavipriya, Manjunatha Hiremath, "Advanced Computational Method to Extract Heart Artery Region," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 366-378, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P237
Coronary artery disease, also known as coronary heart disease, is the thinning or blockage of heart arteries, which is generally caused utilizing the build-up of fatty material called plaque. The coronary angiogram test is currently the most utilized method for identifying the stenosis status of arteries in the heart. The objective of the proposed hybrid segmentation method is to extract the artery region of the heart from angiogram imagery. Numerous angiogram video clips have been considered in the dataset in this research work. These video clips were acquired from a healthcare center with the due consent of patients and the concerned healthcare personnel. Most angiogram videos consist of unclear images, or the contents are generally not clear, and medical experts fail to acquire accurate information about the damages or blocks formed in arteries due to the same reason. A hybrid computational method to extract well-defined images of heart arteries using Frangi and motion blur features from angiogram imagery has been proposed to address this issue. Fifty patients' information has been used as the dataset for experimentation purposes in this research work. The enhanced Frangi filter is used on the dataset to obtain edge information to enhance the input image based on the Hessian matrix. Further, the motion blur helps in automatically tracking/tracing the pixel direction using the optical flow method. In this method, the complete structure of the artery is extracted. The results, when compared to the existing methods, have proven to be novel and more optimal.
Coronary Angiogram, Artery, Stenosis, Segmentation, Frangi Filter, Motion Blur.
 E. Badila, L. Calmac, D. Zamfir, D. Penes, E. Weiss, and V. Bataila, "The Cardiovascular System and the Coronary Circulation," Springer, Cham, 2017.
 R. J. Bache, "Coronary Artery Disease: Regulation of Coronary Blood Flow," in Coronary Artery Disease, Springer, London, 2015, pp. 57–67.
 D. R. Holmes and D. O. Williams, "Catheter-based treatment of coronary artery disease: past, present, and future.," Circ. Cardiovasc. Interv., vol. 1, no. 1, pp. 60–73, 2008, doi: 10.1161/CIRCINTERVENTIONS.108.783134.
 D. B. Mark et al., "ACCF/ACR/AHA/NASCI/SAIP/SCAI/SCCT 2010 Expert Consensus Document on Coronary Computed Tomographic Angiography. A Report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents," J. Am. Coll. Cardiol., vol. 55, no. 23, pp. 2663–2699, 2010, doi: 10.1016/j.jacc.2009.11.013.
 I. Cruz-Aceves, F. Oloumi, R. M. Rangayyan, J. G. Aviña-Cervantes, and A. Hernandez-Aguirre, "Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization," Biomed. Signal Process. Control, vol. 25, pp. 76–85, 2016, doi: 10.1016/j.bspc.2015.11.001.
 R. Surendiran, M. Thangamani, C. Narmatha, M. Iswarya, "Effective Autism Spectrum Disorder Prediction to Improve the Clinical Traits using Machine Learning Techniques", International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp.343-359, 2022. https://doi.org/10.14445/22315381/IJETT-V70I4P230.
 Y. Tian, Y. Pan, F. Duan, S. Zhao, Q. Wang, and W. Wang, "Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method," Biomed Res. Int., vol. 2016, 2016, doi: 10.1155/2016/3530251.
 S. Mabrouk, C. Oueslati, and F. Ghorbel, "Multiscale Graph Cuts Based Method for Coronary Artery Segmentation in Angiograms," IRBM, vol. 38, no. 3, pp. 167–175, 2017, doi: 10.1016/j.irbm.2017.04.004.
 F. Cervantes-Sanchez, I. Cruz-Aceves, A. Hernandez-Aguirre, S. Solorio-Meza, T. Cordova-Fraga, and J. Gabriel Aviña-Cervantes, “Coronary Artery Segmentation in X-ray Angiogram using Gabor filters and Differential Evolution,” Appl. Radiat. Isot., vol. 138, pp. 18–24, 2018, doi: https://doi.org/10.1016/j.apradiso.2017.08.007.
 K. Jo, J. Kweon, Y. H. Kim, and J. Choi, "Segmentation of the Main Vessel of the Left Anterior Descending Artery Using Selective Feature Mapping in Coronary Angiography," IEEE Access, vol. 7, pp. 919–930, 2019, doi: 10.1109/ACCESS.2018.2886009.
 M. EminTenekeci, H. Pehlivan, and Y. Kaya, "Improving performance of coronary artery segmentation using calculated vessel location from the angiogram."
 A. Carballal et al., "Automatic multiscale vascular image segmentation algorithm for coronary angiography," Biomed. Signal Process. Control, vol. 46, pp. 1–9, 2018, doi: 10.1016/j.bspc.2018.06.007.
 J. L. Lai and Y. Yi, "Key frame extraction based on visual attention model," J. Vis. Commun. Image Represent., vol. 23, no. 1, pp. 114–125, 2012, doi: 10.1016/j.jvcir.2011.08.005.
 M. K. Asha Paul, J. Kavitha, and P. A. Jansi Rani, "Keyframe Extraction Techniques: A Review," Recent Patents Comput. Sci., vol. 11, no. 1, pp. 3–16, doi: 10.2174/2213275911666180719111118.
 A. Jahagirdar and M. Nagmode, "Two level key frame extraction for action recognition using content based adaptive threshold," Int. J. Intell. Eng. Syst., vol. 12, no. 5, pp. 43–52, 2019, doi: 10.22266/ijies2019.1031.05.
 F. Ciompi, O. Pujol, S. Balocco, X. Carrillo, J. Mauri, and P. Radeva, "Automatic Key Frames Detection in Intravascular Ultrasound Sequences," MICCAI Work. …, no. 1, 2011.
 A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 1496, no. February, pp. 130–137, 1998, doi: 10.1007/bfb0056195.
 P. T. H. Truc, M. A. U. Khan, Y. K. Lee, S. Lee, and T. S. Kim, "Vessel enhancement filter using directional filter bank," Comput. Vis. Image Underst., vol. 113, no. 1, pp. 101–112, 2009, doi: 10.1016/j.cviu.2008.07.009.
 R. Surendiran, K. Duraisamy, "An Approach in Semantic Web Information Retrieval," IJETT International Journal of Electronics and Communication Engineering, vol. 1, no. 1, pp.17-21, 2014. https://doi.org/10.14445/23488549/IJECE-V1I1P105.
 R. Moreno and Ö. Smedby, "Gradient-based enhancement of tubular structures in medical images," Med. Image Anal., vol. 26, no. 1, pp. 19–29, 2015, doi: 10.1016/j.media.2015.07.001.
 Z. Li, Y. Zhang, G. Liu, H. Shao, W. Li, and X. Tang, "A robust coronary artery identification and centerline extraction method in angiographies," Biomed. Signal Process. Control, vol. 16, pp. 1–8, 2015, doi: 10.1016/j.bspc.2014.09.015.
 F. Zhang, X. Zhang, X. Liu, K. Cao, H. Du, and Y. Cui, "Blood vessel enhancement for DSA images based on adaptive multiscale filtering," Optik (Stuttg)., vol. 125, no. 10, pp. 2383–2388, 2014, doi: 10.1016/j.ijleo.2013.10.111.
 M. E. Martinez-Perez, A. D. Hughes, S. A. Thom, A. A. Bharath, and K. H. Parker, "Segmentation of blood vessels from red-free and fluorescein retinal images," Med. Image Anal., vol. 11, no. 1, pp. 47–61, 2007, doi: 10.1016/j.media.2006.11.004.
 L. Gong, X. Du, C. Lin, K. Zhu, C. Liu, and W. Liang, "Automated High-Resolution Structure Analysis of Plant Root with a Morphological Image Filtering Algorithm," Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/4021426.
 K. Chen, Q. Yin, X. Jia, and M. Lu, "Image Enhancement based Improved Multiscale Hessian Matrix for Coronary Angiography," Int. J. Comput. Appl., vol. 126, no. 10, pp. 1–4, 2015, doi: 10.5120/ijca2015906196.
 L. C. Rodrigues and M. Marengoni, "Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multiscale filtering," Biomed. Signal Process. Control, vol. 36, pp. 39–49, 2017, doi: 10.1016/j.bspc.2017.03.014.
 B. Peng, Y. Wang, and X. Yang, "A multiscale morphological approach to local contrast enhancement for ultrasound images," Proc. - 2010 Int. Conf. Comput. Inf. Sci. ICCIS 2010, vol. 80, pp. 1142–1145, 2010, doi: 10.1109/ICCIS.2010.282.
 G. S. Tejaswi D. Prakash, Deepthi Rajashekar, "Comparison Of Algorithms For Segmentation Of Blood Vessels In Fundus Images," 2nd Int. Conf. Appl. Theor. Comput. Commun. Technol., pp. 114–118, 2016.
 E. Moghimirad, S. Hamid Rezatofighi, and H. Soltanian-Zadeh, “Retinal vessel segmentation using a multi-scale medialness function,” Comput. Biol. Med., vol. 42, no. 1, pp. 50–60, 2012, doi: 10.1016/j.compbiomed.2011.10.008.
 Cruz-Aceves, Ivan, et al. "A novel Gaussian matched filter based on entropy minimization for automatic segmentation of coronary angiograms." Computers & Electrical Engineering 53 (2016): 263-275.
 Cruz-Aceves, Ivan, Arturo Hernandez-Aguirre, and S. Ivvan Valdez. "On the performance of nature inspired algorithms for the automatic segmentation of coronary arteries using Gaussian matched filters." Applied Soft Computing 46 (2016): 665-676.
 Cervantes-Sanchez, Fernando, et al. "Automatic segmentation of coronary arteries in X-ray angiograms using multiscale analysis and artificial neural networks." Applied Sciences 9.24 (2019): 5507.
 B. Toptaş and D. Hanbay, "Retinal blood vessel segmentation using pixel-based feature vector," Biomed. Signal Process.
 Control, vol. 70, no. April, 2021, doi: 10.1016/j.bspc.2021.103053.
 G. Ma, J. Yang, and H. Zhao, "A coronary artery segmentation method based on region growing with variable sector search area," Technol. Heal. Care, vol. 28, no. S1, pp. S463–S472, 2020, doi: 10.3233/THC-209047.
 R. Surendiran, R. Aarthi, M. Thangamani, S. Sugavanam, R. Sarumathy, " A Systematic Review using Machine Learning Algorithms for Predicting Preterm Birth," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp.46-59, 2022. https://doi.org/10.14445/22315381/IJETT-V70I5P207.
 F. Cervantes-Sanchez et al., "Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm," Comput. Intell. Neurosci., vol. 2016, 2016, doi: 10.1155/2016/2420962.
 F. Cervantes-Sanchez, I. Cruz-Aceves, A. Hernandez-Aguirre, F. Cervantes-Sanchez, and I. Cruz-Aceves, "Automatic detection of coronary artery stenosis in X-ray angiograms using Gaussian filters and genetic algorithms," vol. 020005, no. June 2016, doi: 10.1063/1.4954092.
 R. Surendiran, K.Alagarsamy, "Skin Detection Based Cryptography in Steganography (SDBCS)," International Journal of Computer Science and Information Technologies, vol. 1, no. 4, pp.221-225, 2010.
 N. H. H. Thanh, Dang NH, Dvoenko Sergey, V. B. Surya Prasath, "Blood Vessels Segmentation Method For Retinal Fundus Images Based On Adaptive Principal Curvature And Image Derivative," Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLII, no. May, pp. 13–15, 2019.