Automated fuzzy optic disc detection algorithm using branching of vessels and color properties in fundus images

• Autorzy: Nergiz M. , Akın M. , Yıldız A. , Takeş Ö.

Identyfikatory

DOI

10.1016/j.bbe.2018.08.003

• Języki publikacji: EN

Abstrakty

EN

Optic disc (OD) detection is a basic procedure for the image processing algorithms which intend to diagnose and track retinal disorders. In this study, a new OD localization approach is proposed, based on color and shape properties of OD as well as the convergence point of the main vessels. This study is comprised of two successive fundamental steps. At the first step, an algorithm finding the approximate convergent point of the vessels is used in order to roughly localize OD. At the second step, three new features are suggested and a fuzzy logic controller (FLC) whose input membership functions are designed based on these features is proposed. The proposed method is applied to the DRIVE, STARE, DIARETDB0 and DIRETDB1 datasets and the obtained results validate the improvement in the performance by attaining success rate of 100%, 91,35%, 90% and 100% respectively and detecting OD centers and contours precisely in a reasonable execution time.

Słowa kluczowe

EN

optic disc detection; fuzzy logic controller; vessel convergence; fundus image; feature extraction

PL

wykrywanie dysku optycznego; regulator rozmyty; konwergencja naczyniowa; obraz dna oka; ekstrakcja cech

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 4
• Strony: 850--867
• Opis fizyczny: Bibliogr. 57 poz., rys., tab., wykr.

Twórcy

autor

Nergiz M.

• Department of Computer Engineering, Dicle University, Diyarbakir 21280, Turkey

autor

Akın M.

• Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey

autor

Yıldız A.

• Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey

autor

Takeş Ö.

• Çiğli Training and Research Hospital, Izmir, Turkey

Bibliografia

• [1] Youssif AAHAR, Ghalwash AZ, Ghoneim AASAR. Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter. IEEE Trans Med Imaging 2008;27(1):11–8. http://dx.doi.org/10.1109/TMI.2007.900326.
• [2] Fundus photography, http://en.wikipedia.org/wiki/Fundus_photography (accessed 10.01.18).
• [3] Allam AMN, Youssif AA-H, Ghalwash AZ. Automatic segmentation of optic disc in eye fundus images: a survey. Electron Lett Comput Vis Image Anal 2015;14(1):1–20. http://dx.doi.org/10.5565/rev/elcvia.680.
• [4] WHO Diabetes, http://www.who.int/mediacentre/factsheets/fs312/en/, 2017, November (accessed 10.07.18).
• [5] Muangnak N, Aimmanee P, Makhanov S. Automatic optic disk detection in retinal images using hybrid vessel phase portrait analysis. Med Biol Eng Comput 2018;56(4):583–98. http://dx.doi.org/10.1007/s11517-017-1705-z.
• [6] Rodrigues LC, Marengoni M. Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. Biomed Signal Process Control 2017;36:39–49. http://dx.doi.org/10.1016/j.bspc.2017.03.014.
• [7] Zahoor MN, Fraz MM. Fast optic disc segmentation in retina using polar transform. IEEE Access 2017;5:12293–300. http://dx.doi.org/10.1109/ACCESS.2017.2723320.
• [8] Abramoff MD, Garvin MK, Sonka M. Retinal imaging and image analysis. IEEE Rev Biomed Eng 2010;3:169–208. http://dx.doi.org/10.1109/RBME.2010.2084567.
• [9] Fan Z, Rong Y, Cai X, Lu J, Li W, Lin H, et al. Optic disk detection in fundus image based on structured learning. IEEE J Biomed Health Inform 2018;22(1):224–34. http://dx.doi.org/10.1109/JBHI.2017.2723678.
• [10] Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng E, Laude A. Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med 2013;43(12):2136–55. http://dx.doi.org/10.1016/j.compbiomed.2013.10.007.
• [11] Schaefer G, Clos A. Image analysis for exudate detection in retinal images. Biocomput Biomed Inform: Case Stud Appl 2010;198–203. http://dx.doi.org/10.4018/978-1-60566-768-3.ch013.
• [12] M.D. Abramoff, M. Niemeijer, X. Xu, M. Sonka, Automated determination of arteriovenous ratio in images of blood vessels, EP Patent App. EP20,120,736,290.
• [13] Al-Bander B, Al-Nuaimy W, Williams BM, Zheng Y. Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc. Biomed Signal Process Control 2018;40:91–101. http://dx.doi.org/10.1016/j.bspc.2017.09.008.
• [14] Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S. Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 2017;20:70–9. http://dx.doi.org/10.1016/j.jocs.2017.02.006.
• [15] Díaz-Pernil D, Fondón I, Peñ-Cantillana F, Gutiérrez-Naranjo MA. Fully automatized parallel segmentation of the optic disc in retinal fundus images. Pattern Recogn Lett 2016;83:99–107. http://dx.doi.org/10.1016/j.patrec.2016.04.025. geometric, topological and harmonic trends to image processing.
• [16] Alshayeji M, Al-Roomi SA, Abed S. Optic disc detection in retinal fundus images using gravitational law-based edge detection. Med Biol Eng Comput 2017;55(6):935–48. http://dx.doi.org/10.1007/s11517-016-1563-0.
• [17] Dai B, Wu X, Bu W. Optic disc segmentation based on variational model with multiple energies. Pattern Recogn 2017;64:226–35. http://dx.doi.org/10.1016/j.patcog.2016.11.017.
• [18] Hou K, Liu N, Jia W, He Y, Lian J, Zheng Y. Optic disc detection from fundus photography via best-buddies similarity. Appl Sci 2018;8(5). http://dx.doi.org/10.3390/app8050709.
• [19] Lalonde M, Beaulieu M, Gagnon L. Fast and robust optic disc detection using pyramidal decomposition and hausdorff-based template matching. IEEE Trans Med Imaging 2001;20(11):1193–200.
• [20] Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, et al. Optic nerve head segmentation. IEEE Trans Med Imaging 2004;23(2):256–64. http://dx.doi.org/10.1109/TMI.2003.8232.61.
• [21] Wang Y, Zhang H, Yang F. A weighted sparse neighbourhood-preserving projections for face recognition. IETE J Res 2017;63(3):358–67. http://dx.doi.org/10.1080/03772063.2016.1274240.
• [22] Zhang H, Cao L, Gao S. A locality correlation preserving support vector machine. Pattern Recogn 2014;47(9):3168–78. http://dx.doi.org/10.1016/j.patcog.2014.04.004.
• [23] Grewal PS, Oloumi F, Rubin U, Tennant MT. Deep learning in ophthalmology: a review. Can J Ophthalmol 2018. http://dx.doi.org/10.1016/j.jcjo.2018.04.019.
• [24] Nergiz M, Akın Arı M. A fast optic disc localization algorithm based on geometry of vessel distribution. International Conference on Natural Science and Engineering (ICNASE'16); 2016. http://dx.doi.org/10.13140/R.G.2.1.2312.4889.
• [25] Drive, http://www.isi.uu.nl/Research/Databases/DRIVE (accessed 22.02.18).
• [26] Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 2004;23 (4):501–9. http://dx.doi.org/10.1109/TMI.2004.825627.
• [27] Zhu X, Rangayyan RM, Ells AL. Detection of the optic nerve head in fundus images of the retina using the Hough transform for circles. J Digital Imaging 2010;23(3):332–41. http://dx.doi.org/10.1007/s10278-009-9189-5.
• [28] Rangayyan RM, Zhu X, Ayres FJ, Ells AL. Detection of the optic nerve head in fundus images of the retina with Gabor filters and phase portrait analysis. J Digital Imaging 2010;23 (4):438–53. http://dx.doi.org/10.1007/s10278-009-9261-1.
• [29] Stare, http://cecas.clemson.edu/ahoover/stare/nerve/index.html (accessed on 13.07.18).
• [30] Hoover AD, 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.
• [31] Diaretdb1, http://www.it.lut.fi/project/imageret/diaretdb1/ accessed (22.02.18).
• [32] Kauppir T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Raninen A, et al. Diaretdb1 diabetic retinopathy database and evaluation protocol. Med Image Understand Anal 2010;61. http://dx.doi.org/10.5244/C.21.15.
• [33] Diaretdb0, http://www.it.lut.fi/project/imageret/diaretdb0/ (accessed 22.02.18).
• [34] T. Kauppir, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Kälviäinen, J. Pietilä, Diaretdb0: Evaluation database and methodology for diabetic retinopathy algorithmsl, Technical report.
• [35] Nergiz M, Akın Arı M. Diyabetik Retinopati tespitinde yeni bir algoritma kullanılarak optik disk yerinin kestirimi. Dicle Üniveristesi Mühendislik Dergisi 2013;2(412):109–20.
• [36] Nergiz M, Akın Arı M. Optic disc detection in retinal images via a new algorithm; 2014;74–7.
• [37] Zuiderveld K. Contrast limited adaptive histogram equalization. In: Heckbert PS, editor. Graphics Gems IV. San Diego, CA, USA: Academic Press Professional, Inc.; 1994. p. 474–85, http://dl.acm.org/citation.cfm?id=180895.1809.40.
• [38] Frangi AF, Niessen WJ, Vincken KL, Viergever MA. Multiscale vessel enhancement filtering. In: Wells WM, Colchester A, Delp S, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI'98. Berlin, Heidelberg: Springer Berlin Heidelberg; 1998. p. 130–7. http://dx.doi.org/10.1007/BFb0056195.
• [39] Nergiz M, Akın M. Retinal vessel segmentation via structure tensor coloring and anisotropy enhancement. Symmetry 2017;9(11):276–84. http://dx.doi.org/10.3390/sym9110276, http://www.mdpi.com/2073-8994/9/11/276.
• [40] Reza AM. Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. J VLSI Signal Process Syst Signal Image Video Technol 2004;38(1):35–44. http://dx.doi.org/10.1023/B:VLSI.0000028532.53893.82.
• [41] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9(1):62–6. http://dx.doi.org/10.1109/TSMC.1979.4310076.
• [42] Santos BS, Ferreira C, Silva JS, Teixeira ASL. Quantitative evaluation of a pulmonary contour segmentation algorithm in X-ray computed tomography images. Acad Radiol 2004;11(8):868–78. http://dx.doi.org/10.1016/j.acra.2004.05.004.
• [43] Antunes SG, Silva JS, Santos JB, Martins P, Castela E. Phase symmetry approach applied to children heart chambers segmentation: a comparative study. IEEE Trans Biomed Eng 2011;58(8):2264–71. http://dx.doi.org/10.1109/TBME.2011.2144982.
• [44] Silva JS, Santos JB, Roxo D, Martins P, Castela E, Martins R. Algorithm versus physicians variability evaluation in the cardiac chambers extraction. IEEE Trans Inf Technol Biomed 2012;16(5):835–41. http://dx.doi.org/10.1109/TITB.2012.2201949.
• [45] Dehghani A, Moin M-S, Saghafi M. Localization of the optic disc center in retinal images based on the Harris corner detector. Biomed Eng Lett 2012;2(3):198–206. http://dx.doi.org/10.1007/s13534-012-2201-9.
• [46] Harangi B, Hajdu A. Detection of the optic disc in fundus images by combining probability models. Comput Biol Med 2015;65:10–24. http://dx.doi.org/10.1016/j.compbiomed.2015.07.002, http://www.sciencedirect.com/science/article/pii/S0010482515002449.
• [47] Mahfouz AE, Fahmy AS. Fast localization of the optic disc using projection of image features. IEEE Trans Image Process 2010;19(12):3285–9. http://dx.doi.org/10.1109/TIP.2010.2052280.
• [48] Mithun NC, Das S, Fattah SA. Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique. 16th International Conference on Computer and Information Technology (ICCIT). 2014. pp. 98–102. http://dx.doi.org/10.1109/ICCITechn.2014.6997365.
• [49] Qureshi RJ, Kovacs L, Harangi B, Nagy B, Peto T, Hajdu A. Combining algorithms for automatic detection of optic disc and macula in fundus images. Comput Vis Image Understand 2012;116(1):138–45. http://dx.doi.org/10.1016/j.cviu.2011.09.001. virtual Representations and Modeling of Large-scale Environments (VRML). http://www.sciencedirect.com/science/article/pii/S1077314211001883.
• [50] Welfer D, Scharcanski J, Marinho DR. A morphologic two-stage approach for automated optic disk detection in color eye fundus images. Pattern Recogn Lett 2013;34(5):476–85. http://dx.doi.org/10.1016/j.patrec.2012.12.011, http://www.sciencedirect.com/science/article/pii/S0167865512004072.
• [51] Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff MD. Comparative study of retinal vessel segmentation methods on a new publicly available database. Proc SPIE 2004;5370:5370–9. http://dx.doi.org/10.1117/12.535349.
• [52] Bharkad SD. Automatic segmentation of optic disk in retinal images using dwt. IEEE 6th International Conference on Advanced Computing (IACC). 2016. pp. 386–91. http://dx.doi.org/10.1109/IACC.2016.78.
• [53] Nimbarte N, Mushrif M. Extraction of blood vessels and optic disc in retinal images. Comput Methods Biomech Biomed Eng: Imaging Visual 2016;6(1):1–12. http://dx.doi.org/10.1080/21681163.2016.1154806.
• [54] Rahebi J, Hardalaç F. A new approach to optic disc detection in human retinal images using the firefly algorithm. Med Biol Eng Comput 2016;54(2):453–61. http://dx.doi.org/10.1007/s11517-015-1330-7.
• [55] Xiong L, Li H. An approach to locate optic disc in retinal images with pathological changes. Comput Med Imaging Graph 2016;47:40–50. http://dx.doi.org/10.1016/j.compmedimag.2015.10.003, http://www.sciencedirect.com/science/article/pii/S0895611115001445.
• [56] Akyol K, Şen B, Bayir A. Automatic detection of optic disc in retinal image by using keypoint detection, texture analysis, and visual dictionary techniques, computational and mathematical methods in medicine; 2016. http://dx.doi.org/10.1155/2016/6814791.
• [57] Basit A, Fraz MM. Optic disc detection and boundary extraction in retinal images. Appl Opt 2015;54(11):3440–7. http://dx.doi.org/10.1364/AO.54.003440, http://ao.osa.org/abstract.cfm?URI=ao-54-11-3440.