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access icon free Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database

Automatic assessment of retinal vessels plays an important role in the diagnosis of various eye, as well as systemic diseases. A public screening is highly desirable for prompt and effective treatment, since such diseases need to be diagnosed at an early stage. Automated and accurate segmentation of the retinal blood vessel tree is one of the challenging tasks in the computer-aided analysis of fundus images today. We improve the concept of matched filtering, and propose a novel and accurate method for segmenting retinal vessels. Our goal is to be able to segment blood vessels with varying vessel diameters in high-resolution colour fundus images. All recent authors compare their vessel segmentation results to each other using only low-resolution retinal image databases. Consequently, we provide a new publicly available high-resolution fundus image database of healthy and pathological retinas. Our performance evaluation shows that the proposed blood vessel segmentation approach is at least comparable with recent state-of-the-art methods. It outperforms most of them with an accuracy of 95% evaluated on the new database.

References

    1. 1)
      • 21. Lupascu, A.C., Tegolo, D., Trucco, E.: ‘FABC: Retinal vessel segmentation using AdaBoost’, IEEE Trans. Inf. Technol. Biomed., 2010, 14, (5), pp. 12671274 (doi: 10.1109/TITB.2010.2052282).
    2. 2)
      • 15. Fraz, M.M., Remagnino, P., Hoppe, A., et al: ‘Blood vessel segmentation methodologies in retinal images – a survey’, Comput. Methods Programs Biomed., 2012, 108, (1), pp. 407433 (doi: 10.1016/j.cmpb.2012.03.009).
    3. 3)
      • 6. Wang, S., Xu, L., Wang, Y., Jonas, J.B.: ‘Retinal vessel diameter in normal and glaucomatous eyes: the Beijing eye study’, Clin. Exp. Ophth., 2007, 35, (9), pp. 800807 (doi: 10.1111/j.1442-9071.2007.01627.x).
    4. 4)
      • 29. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: ‘Detection of blood vessels in retinal images using two–dimensional matched filters’, IEEE Trans. Med. Imag., 1989, 8, (3), pp. 263269 (doi: 10.1109/42.34715).
    5. 5)
      • 4. Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F.: ‘Automated microaneurysm detection using local contrast normalization and local vessel detection’, IEEE Trans. Med. Imaging, 2006, 25, (9), pp. 12231232 (doi: 10.1109/TMI.2006.879953).
    6. 6)
      • 7. Calvo, D., Ortega, M., Penedo, G.M., Rouco, J.: ‘Automatic detection and characterization of retinal vessel tree bifurcations and crossovers in eye fundus images’, Comput. Methods Programs Biomed., 2011, 103, (1), pp. 2338 (doi: 10.1016/j.cmpb.2010.06.002).
    7. 7)
      • 31. Cinsdikici, M.G., Aydin, D.: ‘Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm’, Comput. Methods Programs Biomed., 2009, 96, (2), pp. 8595 (doi: 10.1016/j.cmpb.2009.04.005).
    8. 8)
      • 36. Kittler, J., Illingworth, J.: ‘Minimum error thresholding’, Pattern Recogn., 1986, 19, (1), pp. 4147 (doi: 10.1016/0031-3203(86)90030-0).
    9. 9)
      • 28. Soares, J.V.B., Leandro, J.J.G., Cesar Jr., R.M., Jelinek, H.F., Cree, M.J.: ‘Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification’, IEEE Trans. Med. Imaging, 2006, 25, (9), pp. 12141222 (doi: 10.1109/TMI.2006.879967).
    10. 10)
      • 16. Vlachos, M., Dermatas, E.: ‘Multi–scale retinal vessel segmentation using line tracking’, Comput. Med. Imaging Graph., 2010, 34, (3), pp. 213227 (doi: 10.1016/j.compmedimag.2009.09.006).
    11. 11)
      • 25. Al-Diri, B., Hunter, A., Steel, D.: ‘An active contour model for segmenting and measuring retinal vessels’, IEEE Trans. Med. Imaging, 2009, 28, (9), pp. 14881497 (doi: 10.1109/TMI.2009.2017941).
    12. 12)
      • 22. Delibasis, K.K., Kechriniotis, I.A., Tsonos, C., Assimakis, N.: ‘Automatic model-based tracing algorithm for vessel segmentation and diameter estimation’, Comput. Methods Programs Biomed., 2010, 100, (2), pp. 108122 (doi: 10.1016/j.cmpb.2010.03.004).
    13. 13)
      • 39. Chanwimaluang, T., Fan, G.: ‘An efficient blood vessel detection algorithm for retinal images using local entropy thresholding’. Proc. Int. Symp. Circuits Systems 2003, Bangkok, Thailand, May 2003, pp. 2124.
    14. 14)
      • 24. Zhu, T.: ‘Fourier cross-sectional profile for vessel detection on retinal images’, Comput. Med. Imaging Graph., 2010, 34, (3), pp. 203212 (doi: 10.1016/j.compmedimag.2009.09.004).
    15. 15)
      • 26. Palomera-Pérez, M.A., Martinez-Perez, M.E., Benítez-Pérez, H., Ortega-Arjona, J.L.: ‘Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection’, IEEE Trans. Inf. Technol. Biomed., 2010, 14, (2), pp. 500506 (doi: 10.1109/TITB.2009.2036604).
    16. 16)
      • 14. Hoover, A., Kouznetsova, V., Goldbaum, M.H.: ‘Locating blood vessels in retinal images by piece–wise threshold probing of a matched filter response’, IEEE Trans. Med. Imaging, 2000, 19, (3), pp. 203210 (doi: 10.1109/42.845178).
    17. 17)
      • 34. Niemann, H., Chrastek, R., Lausen, B., et al: ‘Towards automated diagnostic evaluation of retina images’, Pattern Recogn. Image Anal., 2006, 16, (4), pp. 671676 (doi: 10.1134/S1054661806040146).
    18. 18)
      • 38. Otsu, N.: ‘A threshold selection method from gray–level histograms’, IEEE Trans. Systems Man Cybernet., 1979, 9, (1), pp. 6266 (doi: 10.1109/TSMC.1979.4310076).
    19. 19)
      • 3. Wang, J.J., Liew, G., Klein, R., Rochtchina, E., Knudtson, M.D.: ‘Retinal vessel diameter and cardiovascular mortality: pooled data analysis from two older populations’, Eur. Heart J., 2007, 28, (16), pp. 19841992 (doi: 10.1093/eurheartj/ehm221).
    20. 20)
      • 12. Odstrcilik, J., Jan, J., Kolar, R., Gazarek, J.: ‘Improvement of vessel segmentation by matched filtering in colour retinal images’. Proc. World Congress on Med. Physics and Biomed. Eng., Munich, Germany, September 2009, pp. 327330.
    21. 21)
      • 10. Muramatsu, Ch., Hayashi, Y., Sawada, A., et al: ‘Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma’, J. Biomed. Opt., 2010, 15, (1), pp. 17 (doi: 10.1117/1.3322388).
    22. 22)
      • 33. Budai, A., Michelson, G., Hornegger, J.: ‘Multiscale blood vessel segmentation in retinal fundus images’. Proc. Bildverarbeitung für die Med. 2010 – Alg., Syst., Anwendungen, Aachen, Germany, March 2010, pp. 261265.
    23. 23)
      • 23. Lam, B.S.Y., Gao, Y., Wee-Chung-Liew, A.: ‘General retinal vessel segmentation using regularization-based multiconcavity modeling’, IEEE Trans. Med. Imaging, 2010, 29, (7), pp. 13691380 (doi: 10.1109/TMI.2010.2043259).
    24. 24)
      • 13. Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., Ginneken, B.: ‘Ridge-based vessel segmentation in color images of the retina’, IEEE Trans. Med. Imaging, 2004, 23, (4), pp. 501509 (doi: 10.1109/TMI.2004.825627).
    25. 25)
      • 40. Fawcett, T.: ‘An introduction to ROC analysis’, Pattern Recogn. Lett., 2006, 27, (8), pp. 861874 (doi: 10.1016/j.patrec.2005.10.010).
    26. 26)
      • 11. Han, S., Xu, Z., Sun, Ch.: ‘The recognition based on band tree for blood vessel of ocular fundus’. Proc. IEEE Int. Conf. on Mechatronics and Automation, Changchun, China, August 2009, pp. 33483353.
    27. 27)
      • 17. Tramontan, L., Poletti, E., Fiorin, D., Ruggeri, A.: ‘A web-based system for the quantitative and reproducible assessment of clinical indexes from the retinal vasculature’, IEEE Trans. Biomed. Eng., 2011, 58, (3), pp. 818821 (doi: 10.1109/TBME.2010.2085001).
    28. 28)
      • 1. Ciulla, T.A., Regillo, C.D., Harris, A.H.: ‘Retina and optic nerve imaging’ (Lippincott Williams and Wilkins, 2003), pp. 369.
    29. 29)
      • 35. Dierckx, P.: ‘Curve and surfaces fitting with splines’ (Oxford University Press, 1996).
    30. 30)
      • 19. Ricci, E., Perfetti, R.: ‘Retinal blood vessel segmentation using line operators and support vector classification’, IEEE Trans. Med. Imaging, 2007, 26, (10), pp. 13571365 (doi: 10.1109/TMI.2007.898551).
    31. 31)
      • 30. Al-Rawi, M., Karajeh, H.: ‘Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images’, Comput. Methods Programs Biomed., 2007, 87, (3), pp. 248253 (doi: 10.1016/j.cmpb.2007.05.012).
    32. 32)
      • 41. Niemeijer, M., Ginneken, B., Russell, S.R., Suttorp-Schulten, M.S.A., Abramoff, M.D.: ‘Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diangosis’, Invest. Ophthalmol. Vis. Sci., 2007, 48, (5), pp. 22602267 (doi: 10.1167/iovs.06-0996).
    33. 33)
      • 20. Marín, D., Aquino, A., Gegúndez–Arias, M.E., Bravo, J.M.: ‘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), pp. 146158 (doi: 10.1109/TMI.2010.2064333).
    34. 34)
      • 2. Bock, R., Meier, J., Nyúl, L.G., Hornegger, J., Michelson, G.: ‘Glaucoma risk index: automated glaucoma detection from color fundus images’, Med. Image Anal., 2010, 14, (3), pp. 471481 (doi: 10.1016/j.media.2009.12.006).
    35. 35)
      • 27. Mendonca, A.M., Campilho, A.: ‘Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction’, IEEE Trans. Med. Imaging, 2006, 25, (9), pp. 12001213 (doi: 10.1109/TMI.2006.879955).
    36. 36)
      • 9. Niemeijer, M., Abramoff, M.D., Ginneken, B.: ‘Segmentation of the optic disc, macula and vascular arch in fundus photographs’, IEEE Trans. Med. Imaging, 2007, 26, (1), pp. 116127 (doi: 10.1109/TMI.2006.885336).
    37. 37)
      • 32. Zhang, B., Zhang, L., Zhang, L., Karray, F.: ‘Retinal vessel extraction by matched filter with first–order derivative of Gaussian’, Comput. Biol. Med., 2010, 40, (4), pp. 438445 (doi: 10.1016/j.compbiomed.2010.02.008).
    38. 38)
      • 8. Kolar, R., Kubecka, L., Jan, J.: ‘Registration and fusion of the autofluorescent and infrared retinal images’, Int. J. Biomed. Imaging, 2008, 2008, pp. 111 (doi: 10.1155/2008/513478).
    39. 39)
      • 5. Grisan, E., Foracchia, M., Ruggeri, A.: ‘A novel method for the automatic grading of retinal vessel tortuosity’, IEEE Trans. Med. Imaging, 2008, 27, (3), pp. 310319 (doi: 10.1109/TMI.2007.904657).
    40. 40)
      • 18. Giani, A., Grisan, E., Ruggeri, A.: ‘Enhanced classification–based vessel tracking using vessel models and Hough transform’. Proc. Third Europe on Medical and Biological Engineering Conf. EMBEC 2005, Prague, Czech Republic, November 2005.
    41. 41)
      • 37. Sezgin, M., Sankur, B.: ‘Survey over image thresholding techniques and quantitative performance evaluation’, J. Electron. Imaging, 2004, 13, (1), pp. 146165 (doi: 10.1117/1.1631315).
    42. 42)
      • 42. Akram, M.U., Tariq, A., Anjum, M.A., Younnus-Javed, M.: ‘Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy’, Appl. Opt., 2012, 51, (20), pp. 48584866 (doi: 10.1364/AO.51.004858).
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