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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-244974f5-ecb5-4fa4-a889-f59f38764bb6

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

Fast, accurate and robust retinal vessel segmentation system

Autorzy Jiang, Z.  Yepez, J.  An, S.  Ko, S. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN The accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic ophthalmological and cardiovascular diagnosis systems. Aside from accuracy, robustness and processing speed are also considered crucial for medical purposes. In order to meet those requirements, this work presents a novel approach to extract blood vessels from the retinal fundus, by using morphology-based global thresholding to draw the retinal venule structure and centerline detection method for capillaries. The proposed system is tested on DRIVE and STARE databases and has an average accuracy of 95.88% for single-database test and 95.27% for the cross-database test. Meanwhile, the system is designed to minimize the computing complexity and processes multiple independent procedures in parallel, thus having an execution time of 1.677 s per image on CPU platform.
Słowa kluczowe
PL obrazowanie siatkówki   segmentacja naczyń krwionośnych   przetwarzanie morfologiczne   analiza automatyczna  
EN retinal image   vessel segmentation   morphological processing   DoOG filter   automated analysis   thresholding  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 412--421
Opis fizyczny Bibliogr. 17 poz., rys., tab., wykr.
Twórcy
autor Jiang, Z.
  • University of Saskatchewan, Department of Electrical and Computer Engineering, 57 Campus Drive, Saskatoon, Canada S7N 5A9
autor Yepez, J.
  • University of Saskatchewan, Department of Electrical and Computer Engineering, 57 Campus Drive, Saskatoon, Canada S7N 5A9
autor An, S.
  • University of Saskatchewan, Department of Electrical and Computer Engineering, 57 Campus Drive, Saskatoon, Canada S7N 5A9
autor Ko, S.
  • University of Saskatchewan, Department of Electrical and Computer Engineering, 57 Campus Drive, Saskatoon, Canada S7N 5A9, seokbum.ko@usask.ca
Bibliografia
[1] Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA. Blood vessel segmentation methodologies in retinal images – a survey. Comput Methods Progr Biomed 2012;108(1):407–33. http://dx.doi.org/10.1016/j.cmpb.2012.03.009.
[2] Koukounis D, Ttofis C, Papadopoulos A, Theocharides T. A high performance hardware architecture for portable, low-power retinal vessel segmentation. Integr VLSI J 2014;47(3):377–86. http://dx.doi.org/10.1016/j.vlsi.2013.11.005.
[3] Krause M, Alles RM, Burgeth B, Weickert J. Fast retinal vessel analysis. J Real-Time Image Process 2013;11(2):413–22. http://dx.doi.org/10.1007/s11554-013-0342-5.
[4] Staal J, Abràmoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 2005;23 (4):501–9. http://dx.doi.org/10.1109/TMI.2004.825627.
[5] Soares JVB, Leandro JJG, Cesar Jr RM, Jelinek HF, Cree MJ. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 2006;25(9):1214–22. http://dx.doi.org/10.1109/TMI.2006.879967.
[6] Ricci E, Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging 2007;26(10):1357–65. http://dx.doi.org/10.1109/TMI.2007.898551.
[7] Lupasscu CA, Tegolo D, Trucco E. FABC: retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed 2010;14(5):1267–74. http://dx.doi.org/10.1109/TITB.2010.2052282.
[8] Marín D, Aquino A, Gegúndez-Arias ME, Bravo JM. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging 2011;30(1):146–58. http://dx.doi.org/10.1109/TMI.2010.2064333.
[9] Villalobos-Castaldi FM, Felipe-Riverón EM, Sánchez- Fernández LP. A fast, efficient and automated method to extract vessels from fundus images. J Vis 2010;13(3):263–70. http://dx.doi.org/10.1007/s12650-010-0037-y.
[10] Mendonca AM, Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 2006;25(9):1200–13. http://dx.doi.org/10.1109/TMI.2006.879955.
[11] Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA. An ensemble classification- based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 2012;59(9):2538–48.
[12] Fraz MM, Barman SA, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, Rudnicka AR, Owen CG. An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Programs Biomed 2012;108(2):600–16. http://dx.doi.org/10.1016/j.cmpb.2011.08.009.
[13] Abdurrazaq I, Hati S, Eswaran C. Morphology approach for features extraction in retinal images for diabetic retinopathy diagnosis. Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development; 2008. p. 1373–7. http://dx.doi.org/10.1109/ICCCE.2008.4580830.
[14] Sofka M, Stewart CV. Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Trans Med Imaging 2006;25(12):1531–46. http://dx.doi.org/10.1109/TMI.2006.884190.
[15] Xu L, Luo S. A novel method for blood vessel detection from retinal images. Biomed Eng Online 2010;9:14. http://dx.doi.org/10.1186/1475-925X-9-14.
[16] Lam BSY, Gao Y, Liew AW. General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging 2010;29(7):1369–81. http://dx.doi.org/10.1109/TMI.2010.2043259.
[17] Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 2000;19 (3):203–10. http://dx.doi.org/10.1109/42.845178.
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-244974f5-ecb5-4fa4-a889-f59f38764bb6
Identyfikatory
DOI 10.1016/j.bbe.2017.04.001