Journal of Computing Science and Engineering

  • 제8권2호
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  • Pages.119-128
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  • 2014
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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Automatic Segmentation of Retinal Blood Vessels Based on Improved Multiscale Line Detection

  • Hou, Yanli (School of Computer and Information Technology, Shangqiu Normal University)
  • 투고 : 2013.11.23
  • 심사 : 2014.06.02
  • 발행 : 2014.06.30
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초록

The appearance of retinal blood vessels is an important diagnostic indicator of serious disease, such as hypertension, diabetes, cardiovascular disease, and stroke. Automatic segmentation of the retinal vasculature is a primary step towards automatic assessment of the retinal blood vessel features. This paper presents an automated method for the enhancement and segmentation of blood vessels in fundus images. To decrease the influence of the optic disk, and emphasize the vessels for each retinal image, a multidirectional morphological top-hat transform with rotating structuring elements is first applied to the background homogenized retinal image. Then, an improved multiscale line detector is presented to produce a vessel response image, and yield the retinal blood vessel tree for each retinal image. Since different line detectors at varying scales have different line responses in the multiscale detector, the line detectors with longer length produce more vessel responses than the ones with shorter length; the improved multiscale detector combines all the responses at different scales by setting different weights for each scale. The methodology is evaluated on two publicly available databases, DRIVE and STARE. Experimental results demonstrate an excellent performance that approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so it is suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.

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참고문헌

  1. R. Bernardes, P. Serranho, and C. Lobo, "Digital ocular fundus imaging: a review," Ophthalmologica, vol. 226, no. 4, pp. 161-181, 2011. https://doi.org/10.1159/000329597
  2. M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, "An ensemble classification-based approach applied to retinal blood vessel segmentation," IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2538-2548, 2012. https://doi.org/10.1109/TBME.2012.2205687
  3. M. M. Fraz, P. Remagnino, A. Hoppe, and S. A. Barman, "Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier," in Proceedings of the International Conference on Computer Medical Applications, Sousse, Tunisia, 2013, pp. 1-6.
  4. U. T. Nguyen, A. Bhuiyan, L. A. Park, and K. Ramamohanarao, "An effective retinal blood vessel segmentation method using multi-scale line detection," Pattern Recognition, vol. 46, no. 3, pp. 703-715, 2013. https://doi.org/10.1016/j.patcog.2012.08.009
  5. Y. Wang, G. Ji, P. Lin, and E. Trucco, "Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition," Pattern Recognition, vol. 46, no. 8, pp. 2117-2133, 2013. https://doi.org/10.1016/j.patcog.2012.12.014
  6. M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, "Blood vessel segmentation methodologies in retinal images: a survey," Computer Methods and Programs in Biomedicine, vol. 108, no. 1, pp. 407-433, 2012. https://doi.org/10.1016/j.cmpb.2012.03.009
  7. O. Faust, R. Acharya, E. Y. K. Ng, K. H. Ng, and J. S. Suri, "Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review," Journal of Medical Systems, vol. 36, no. 1, pp. 145-157, 2012. https://doi.org/10.1007/s10916-010-9454-7
  8. M. M. Fraz, A. Basit, and S. A. Barman, "Application of morphological bit planes in retinal blood vessel extraction," Journal of Digital Imaging, vol. 26, no. 2, pp. 274-286, 2013. https://doi.org/10.1007/s10278-012-9513-3
  9. Q. Li, J. You, and D. Zhang, "Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses," Expert Systems with Applications, vol. 39, no. 9, pp. 7600-7610, 2012. https://doi.org/10.1016/j.eswa.2011.12.046
  10. F. Zana and J. C. Klein. "Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation," IEEE Transactions on Image Processing, vol. 10, no. 7, pp. 1010-1019, 2001. https://doi.org/10.1109/83.931095
  11. Y. Yin, M. Adel, and S. Bourennane, "Retinal vessel segmentation using a probabilistic tracking method," Pattern Recognition, vol. 45, no. 4, pp. 1235-1244, 2012. https://doi.org/10.1016/j.patcog.2011.09.019
  12. F. Nie and P. Zhang, "Fuzzy partition and correlation for image segmentation with differential evolution," IAENG International Journal of Computer Science, vol. 40, no. 3, pp. 164-172, 2013.
  13. A. Hunter, J. Lowell, R. Ryder, A. Basu, and D. Steel, "Tramline filtering for retinal vessel segmentation," in Proceedings of the 3rd European Medical and Biological Engineering Conference, Prague, Czech Republic, 2005, pp. 1-4.
  14. E. Ricci and R. Perfetti, "Retinal blood vessel segmentation using line operators and support vector classification," IEEE Transactions on Medical Imaging, vol. 26, no. 10, pp. 1357-1365, 2007. https://doi.org/10.1109/TMI.2007.898551
  15. M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abramoff, "Comparative study of retinal vessel segmentation methods on a new publicly available database," in Proceedings of SPIE: Medical Imaging 2004, Bellingham, WA: SPIE, pp. 648-656, 2004.
  16. A. D. Hoover, V. Kouznetsova, and M. Goldbaum, "Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response," IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203-210, 2000. https://doi.org/10.1109/42.845178
  17. J. V. Soares, J. J. Leandro, R. M. Cesar, H. F. Jelinek, and M. J. Cree, "Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification," IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1214-1222, 2006. https://doi.org/10.1109/TMI.2006.879967
  18. D. Marin, A. Aquino, M. E. Gegundez-Arias, and J. M. Bravo, "A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariantsbased features," IEEE Transactions on Medical Imaging, vol. 30, no. 1, pp. 146-158, 2011. https://doi.org/10.1109/TMI.2010.2064333
  19. J. Serra, Image Analysis and Mathematical Morphology, London: Academic Press, 1983.

피인용 문헌

  1. A Review on Recent Developments for Detection of Diabetic Retinopathy vol.2016, 2016, https://doi.org/10.1155/2016/6838976
  2. Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation 2017, https://doi.org/10.1007/s00521-016-2811-9
  3. Retinal Blood Vessel Segmentation Approach Based on Mathematical Morphology vol.65, 2015, https://doi.org/10.1016/j.procs.2015.09.005
  4. Contrast normalization steps for increased sensitivity of a retinal image segmentation method 2017, https://doi.org/10.1007/s11760-017-1114-7
  5. An Improved Method for Automatic Retinal Blood Vessel Vascular Segmentation Using Gabor Filter vol.05, pp.04, 2015, https://doi.org/10.4236/ojmi.2015.54026
  6. Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey 2017, https://doi.org/10.1007/s10044-017-0630-y
  7. A generalized multi-scale line-detection method to boost retinal vessel segmentation sensitivity pp.1433-755X, 2019, https://doi.org/10.1007/s10044-018-0696-1
  8. Segmentation of shallow scratches image using an improved multi-scale line detection approach pp.1573-7721, 2018, https://doi.org/10.1007/s11042-018-6222-z
  9. Impact of ICA-Based Image Enhancement Technique on Retinal Blood Vessels Segmentation vol.6, pp.2169-3536, 2018, https://doi.org/10.1109/ACCESS.2018.2794463