دانشگاه تبریز
فهرست نشریات دارای اعتبار وزارت علوم، تحقیقات و فناوری
پایگاه استنادی علوم جهان اسلام (ISC)

 آمار

تعداد نشریات45
تعداد شماره‌ها1,391
تعداد مقالات17,025
تعداد مشاهده مقاله54,913,168
تعداد دریافت فایل اصل مقاله17,413,408
Bahrami, Tohid, Jabbari Khamnei, Hossein, Kibria, Golam. (1403). Kernel density estimation applications in vessel extraction for MRA images. سامانه مدیریت نشریات علمی, 13(1), 190-200. doi: 10.22034/cmde.2024.60922.2605
Tohid Bahrami; Hossein Jabbari Khamnei; Golam Kibria. "Kernel density estimation applications in vessel extraction for MRA images". سامانه مدیریت نشریات علمی, 13, 1, 1403, 190-200. doi: 10.22034/cmde.2024.60922.2605
Bahrami, Tohid, Jabbari Khamnei, Hossein, Kibria, Golam. (1403). 'Kernel density estimation applications in vessel extraction for MRA images', سامانه مدیریت نشریات علمی, 13(1), pp. 190-200. doi: 10.22034/cmde.2024.60922.2605
Bahrami, Tohid, Jabbari Khamnei, Hossein, Kibria, Golam. Kernel density estimation applications in vessel extraction for MRA images. سامانه مدیریت نشریات علمی, 1403; 13(1): 190-200. doi: 10.22034/cmde.2024.60922.2605

Kernel density estimation applications in vessel extraction for MRA images

Computational Methods for Differential Equations
مقاله 14، دوره 13، شماره 1، فروردین 2025، صفحه 190-200    اصل مقاله (1.83 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2024.60922.2605
نویسندگان
Tohid Bahrami* 1؛ Hossein Jabbari Khamnei* 1؛ Golam Kibria2
1Department of Statistics, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Iran.
2Department of Mathematics and Statistics, Florida International University, FIU, Miami, USA.
چکیده
Vascular-related diseases have become increasingly significant as public health concerns. The analysis of blood vessels plays an important role in detecting and treating diseases. Extraction of vessels is a very important technique in vascular analysis. Magnetic Resonance Angiography (MRA) is a medical imaging technique used to visualize the blood vessels and vascular system in three-dimensional images. These images provide detailed information about the size and shape of the vessels, any narrowing or stenosis, as well as blood supply and circulation in the body. Tracing vessels from medical images is an essential step in the diagnosis and treatment of vascular-related diseases. Many different techniques and algorithms have been proposed for vessel extraction. In this paper, we present a vessel extraction method based on the Kernel density estimation (KDE). Numerical experiments on real 2D MRA images demonstrate that the presented method is very efficient. The effectiveness of the proposed method has been proven through comparative analysis with validated existing methods.
کلیدواژه‌ها
Vessel extraction؛ Kernel density estimation؛ MRA images؛ Bandwidth selection
مراجع
  • [1] K. Andreas, K. Forester Lee, and A. A. Amini, Quantitative coronary angiography with deformable spline models, IEEE transactions on medical imaging, 16(5) (1997), 468-482.
  • [2] X. Cai, R. Chan, S. Morigi, and F. Sgallari, Vessel segmentation in medical imaging using a tight-frame–based algorithm, SIAM Journal on Imaging Sciences, 1 (2013), 464-486.
  • [3] C. Carson, S. Belongie, H. Greenspan, and J. Malik, Blobworld, Image segmentation using expectationmaximization and its application to image querying, IEEE Transactionson pattern analysis and machine intelligence, 24(8) (2002), 1026-38.
  • [4] Y. C. Chen, A tutorial on kernel density estimation and recent advances, Biostatistics & Epidemiology, 1(1) (2017), 161–187.
  • [5] T. Chen, et al., Segmentation of cDNA microarray images by kernel density estimation, Journal of Biomedical informatics, 41(6) (2008), 1021-1027.
  • [6] A. Ciura, Viesha, J. Mark Lee, and C. Drew Schemmer, MRA: current applications in body vascular imaging, Canadian Association of Radiologists’ Journal, 60(3) (2009), 133-142.
  • [7] D. I. Comaniciu, Nonparametric robust methods for computer vision, Rutgers The State University of New Jersey, School of Graduate Studies, (2000).
  • [8] M. A. Creager, et al., Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: Part I, Circulation, 108(12) (2003), 1527-1532.
  • [9] G. Dongbai and P. Richardson, Automatic vessel extraction from angiogram images, Computers in Cardiology, 25 (1998).
  • [10] P. Emanuel, On estimation of a probability density function and mode, The annals of mathematical statistics, 33(3) (1962), 1065-1076.
  • [11] A. Gramacki, Nonparametric Kernel Density Estimation and Its Computational Aspects, Springer: Cham, Switzerland, (2018).
  • [12] A. C. Guidoum, Kernel estimator and bandwidth selection for density and its derivatives, Department of Probabilities and Statistics, University of Science and Technology, Houari Boumediene, Algeria (2015).
  • [13] W. E. Higgins, W. J. T. Spyra, and E. L. Ritman, Automatic extraction of the arterial tree from 3-D angiograms, Images of the Twenty-First Century, Proceedings of the Annual International Engineering in Medicine and Biology Society, IEEE, (1989).
  • [14] B. Hilde, et al., Basic principles of MRA, European journal of radiology, 38(1) (2001), 2-9.
  • [15] J. M. P. C. Holash, et al., Vessel cooption, regression, and growth in tumors mediated by angiopoietins and VEGF, Science, 284(5422) (1999), 1994-1998.
  • [16] O. Ivan Macia, Medical image analysis for the detection, extraction and modelling of vascular structures, Diss. Universidad del Paıs Vasco Euskal Herriko Unibertsitatea, (2012).
  • [17] X. Jiaquan, et al., Deaths: final data for 2007, Natl Vital Stat Rep, 58(19) (2010), 1-19.
  • [18] Y. E. Kent, Magnetic resonance angiography, Perspectives in Vascular Surgery and Endovascular Therapy, 3(1) (1990), 35-47.
  • [19] Y. E. Kent, et al., Magnetic resonance angiography: update on applications for extracranial arteries, Circulation, 100(22) (1999), 2284-2301.
  • [20] C. Kirbas and F. Quek, A review of vessel extraction techniques and algorithms, ACM Computing Surveys (CSUR), 36(2) (2004), 81-121.
  • [21] Q. Li and J. Scott Racine, Nonparametric econometrics: theory and practice, Princeton University Press, (2023).
  • [22] F. Mao, et al., Extraction of structural features in digital subtraction angiography, Proceedings of the 1992 International Biomedical Engineering Days. IEEE, (1992).
  • [23] K. Mohd Imran, et al., A review of retinal vessel segmentation techniques and algorithms, International Journal of Computer Technology and Applications, 2(5) (2011), 1140-1144.
  • [24] Z. Nanning and J. Xue, Statistical learning and pattern analysis for image and video processing, Springer Science & Business Media, (2009).
  • [25] G. Necla and S¸. Karako¸c, Optimal Bandwidth Selection Methods with Application to Wind Speed Distribution, Mathematics, 11(21) (2023), 44-78.
  • [26] Y. W. Nielsen and H. S. Thomsen, Contrast-enhanced peripheral MRA: technique and contrast agents, Acta Radiologica, 53(7) (2012), 769-777.
  • [27] H. Nils-Bastian, A. Schindler, and S. Sperlich, Bandwidth selection for kernel density estimation: a review of fully automatic selectors, AStA Advances in Statistical Analysis, 97(2013), 403-433.
  • [28] N. Noboru, et al., 3D imaging of blood vessels using x-ray rotational angiographic system, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference. IEEE, (1993).
  • [29] P. Riccardo and G. Valli, An algorithm for real-time vessel enhancement and detection, Computer methods and programs in Biomedicine, 52(1) (1997), 1-22.
  • [30] R. Rodrguez and J. H. Sossa, Procesamiento y Anlisis Digital de Imgenes”, (”Processing and Digital Image Analysis”), Book Published by ”Ra-Ma” Editorial, ISBN: 978-84-9964-007-8, May (2011).
  • [31] A. Schindler, Bandwidth selection in nonparametric kernel estimation, Diss. Göttingen, Georg-August Universit¨at, Diss., 2011, 2012.
  • [32] H. Shah, et al., Modern diagnostic imaging technique applications and risk factors in the medical field: A review, BioMed Research International, 2022 (2022).
  • [33] SJ. Sheather and MC. Jones, A reliable data-based bandwidth selection method for kernel density estimation, J Roy Stat Soc B (1991), 683–90.
  • [34] I. Shunsuke, et al., Fully Data-driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score, arXiv preprint arXiv:2212.00984, (2022).
  • [35] B. W. Silverman, Density estimation for statistics and data analysis, Monographs on Statistics and Applied Probability, (1986).
  • [36] R. K. Srinivasa, Probability Distributions in Image Segmentation, Special Proceeding of 20th Annual Conference of SSCA, held at Pondicherry University, Puducherry during January, (2018), 51-69.
  • [37] G. R. Terrell and W. S. David, Variable kernel density estimation, The Annals of Statistics, (1992), 1236-1265.
  • [38] P. Veronique, et al., Thin network extraction in 3D images: application to medical angiograms, Proceedings of 13th International Conference on Pattern Recognition, 3 (1996).
  • [39] D. Wilson and J. A. Noble, Adaptive segmentation algorithm for time-of-flight MRA data, IEEE Trans. Med. Imaging, 18 (10) (1999), 938-945.
  • [40] S. Yoshinobu, et al., 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images, International Conference on Computer Vision, Virtual Reality, and Robotics in Medicine. Berlin, Heidelberg: Springer Berlin Heidelberg, (1997).
آمار
تعداد مشاهده مقاله: 171
تعداد دریافت فایل اصل مقاله: 315


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب