Skip to main content
SpringerLink
Log in

Fusion of Entropy-Based Thresholding and Active Contour Model for Detection of Exudate and Optic Disc in Color Fundus Images

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

This paper proposes an efficient and accurate exudate and optic disc (OD) region segmentation methodology. Exudate, an inflammation that occurs in diabetic retinopathy, must be localized for diabetic retinopathy diagnosis. Similarly, the OD region must be inspected for changes in the macular area. Two methods are proposed for locating exudate and the OD region in color fundus images, respectively. The algorithms are then combined to build a single exudate and OD region segmentation algorithm. The methodology uses color normalization to the green channel color space, an intermediate pre-processing step, and a region segmentation step, where active-contour and entropy-based thresholding techniques are applied for segmenting an image to extract exudate and OD. The proposed method is tested on the MESSIDORe, e-ophtha, DIARETDB1, STARE, Pattern Recognition Lab (CS5), and local databases. The segmented images are compared with ground truth images manually generated by a clinician. The segmentation accuracy is found to be 98%. The algorithm successfully delineates the region of interest from the background.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ranamuka, N. G., & Meegama, R. G. N. (2013). Detection of hard exudates from diabetic retinopathy images using fuzzy logic. IET Image Processing, 7(2), 121–130.

    Article  MathSciNet  Google Scholar 

  2. Kertes, P. J., & Johnson, T. M. (2007). Evidence-based eye care. Philadelphia, PA: Lippincott Williams & Wilkins.

    Google Scholar 

  3. Dehghani, A., Moghaddam, H. A., & Moin, M.-S. (2012). Optic disc localization in retinal images using histogram matching. EURASIP Journal on Image and Video Processing, 2012(1), 1–11.

    Article  Google Scholar 

  4. Kumari, V. V., & Suriyanarayanan, N. (2010). Blood vessel extraction using wiener filter and morphological operation. International Journal of Computer Science & Emerging Technologies, 1(4), 7–10.

    Google Scholar 

  5. Abdel-Ghafar, R., Morris, T., Ritchings, T., & Wood, I. (2004). Detection and characterisation of the optic disk in glaucoma and diabetic retinopathy. In Proceedings of medical image understanding and analysis.

  6. Bjørvig, S., Johansen, M. A., & Fossen, K. (2002). An economic analysis of screening for diabetic retinopathy. Journal of Telemedicine and Telecare, 8(1), 32–35.

    Article  Google Scholar 

  7. Feman, S. S., Leonard-Martin, T. C., Andrews, J. S., Armbruster, C. C., Burdge, T. L., Debelak, J. D., et al. (1995). A quantitative system to evaluate diabetic retinopathy from fundus photographs. Investigative Ophthalmology & Visual Science, 36(1), 174–181.

    Google Scholar 

  8. Winder, R. J., Morrow, P. J., McRitchie, I. N., Bailie, J., & Hart, P. M. (2009). Algorithms for digital image processing in diabetic retinopathy. Computerized Medical Imaging and Graphics, 33(8), 608–622.

    Article  Google Scholar 

  9. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., & Goldbaum, M. (1989). Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging, 8(3), 263–269.

    Article  Google Scholar 

  10. Sinthanayothin, C., Boyce, J. F., Cook, H. L., & Williamson, T. H. (1999). Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology, 83(8), 902–910.

    Article  Google Scholar 

  11. Walter, T., & Klein, J.-C. (2001). Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques. In Medical data analysis (pp. 282–287). Berlin: Springer.

  12. Walter, T., Klein, J.-C., Massin, P., & Erginay, A. (2002). A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Transactions on Medical Imaging, 21(10), 1236–1243.

    Article  Google Scholar 

  13. Abdel-Razik Youssif, A.-H., Ghalwash, A. Z., & Abdel-Rahman Ghoneim, A. (2008). Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Transactions on Medical Imaging, 27(1), 11–18.

    Article  Google Scholar 

  14. ter Haar, F. (2005). Automatic localization of the optic disc in digital colour images of the human retina. Citeseer

  15. Hoover, A., & Goldbaum, M. (2003). Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Transactions on Medical Imaging, 22(8), 951–958.

    Article  Google Scholar 

  16. Wu, D., Zhang, M., Liu, J.-C., & Bauman, W. (2006). On the adaptive detection of blood vessels in retinal images. IEEE Transactions on Biomedical Engineering, 53(2), 341–343.

    Article  Google Scholar 

  17. Xu, J., Chutatape, O., & Chew, P. (2007). Automated optic disk boundary detection by modified active contour model. IEEE Transactions on Biomedical Engineering, 54(3), 473–482.

    Article  Google Scholar 

  18. Chanwimaluang, T., & Fan, G. (2003). An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. In International Symposium on Circuits and Systems ISCAS’03 (vol. 5, pp. V-21–V-24 vol. 25). Piscataway: IEEE

  19. Mittapalli, P. S., & Kande, G. B. (2016). Segmentation of optic disc and optic cup from digital fundus images for the assessment of glaucoma. Biomedical Signal Processing and Control, 24, 34–46. doi:10.1016/j.bspc.2015.09.003.

    Article  Google Scholar 

  20. Kande, G. B., Subbaiah, P. V., & Savithri, T. S. (2008). Segmentation of exudates and optic disc in retinal images. In Sixth Indian Conference on Computer Vision, Graphics & Image Processing ICVGIP’08 (pp. 535–542). Piscataway: IEEE

  21. Li, H., & Chutatape, O. (2004). Automated feature extraction in color retinal images by a model based approach. IEEE Transactions on Biomedical Engineering, 51(2), 246–254.

    Article  Google Scholar 

  22. Li, H., & Chutatape, O. (2003). A model-based approach for automated feature extraction in fundus images. In Ninth IEEE international conference on computer vision (pp. 394–399). Piscataway: IEEE

  23. Li, H., & Chutatape, O. (2001). Automatic location of the optic disc in retinal images. In International conference on image processing (vol. 2, pp. 837–840). Piscataway: IEEE

  24. Hill, D. (1968). A vector clustering technique. Mechanized information storage, retrieval, and dissemination, North-Holland, Amsterdam.

  25. Li, H., & Chutatape, O. (2003). Boundary detection of the optic disk by a modified ASM method. Pattern Recognition, 36(9), 2093–2104.

    Article  MATH  Google Scholar 

  26. Liu, Z., Opas, C., & Krishnan, S. M. (1997). Automatic image analysis of fundus photograph. In Proceedings of the 19th Annual international conference of the IEEE Engineering in Medicine and Biology Society (vol. 2, pp. 524–525). Piscataway: IEEE

  27. Kochner, B., Schuhmann, D., Michaelis, M., Mann, G., & Englmeier, K.-H. (1998). Course tracking and contour extraction of retinal vessels from color fundus photographs: Most efficient use of steerable filters for model-based image analysis. In Medical imaging (pp. 755–761). Bellingham, WA: International Society for Optics and Photonics

  28. Pinz, A., Bernogger, S., Datlinger, P., & Kruger, A. (1998). Mapping the human retina. IEEE Transactions on Medical Imaging, 17(4), 606–619.

    Article  Google Scholar 

  29. Yulong, M., & Dingru, X. (1990). Recognizing glaucoma from ocular fundus image by image processing. In Proceedings of the twelfth annual international conference on IEEE Engineering and Medicine and Biological Society (vol. 12, pp. 178–179)

  30. Xiong, L., & Li, H. (2016). An approach to locate optic disc in retinal images with pathological changes. Computerized Medical Imaging and Graphics, 47, 40–50.

    Article  Google Scholar 

  31. Youssif, A. A.-H. A.-R., Ghalwash, A. Z., & Ghoneim, A. A. S. A.-R. (2008). Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Transactions on Medical Imaging, 27(1), 11–18.

    Article  Google Scholar 

  32. Yazid, H., Arof, H., & Isa, H. M. (2012). Exudates segmentation using inverse surface adaptive thresholding. Measurement, 45(6), 1599–1608.

    Article  Google Scholar 

  33. Sánchez, C. I., García, M., Mayo, A., López, M. I., & Hornero, R. (2009). Retinal image analysis based on mixture models to detect hard exudates. Medical Image Analysis, 13(4), 650–658.

    Article  Google Scholar 

  34. Foracchia, M., Grisan, E., & Ruggeri, A. (2005). Luminosity and contrast normalization in retinal images. Medical Image Analysis, 9(3), 179–190.

    Article  Google Scholar 

  35. McLachlan, G., & Peel, D. (2004). Finite mixture models. New York: Wiley.

    MATH  Google Scholar 

  36. Welfer, D., Scharcanski, J., & Marinho, D. R. (2010). A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Computerized Medical Imaging and Graphics, 34(3), 228–235.

    Article  Google Scholar 

  37. Reza, A. W., Eswaran, C., & Hati, S. (2009). Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds. Journal of Medical Systems, 33(1), 73–80.

    Article  Google Scholar 

  38. Soares, I., Castelo-Branco, M., & Pinheiro, A. M. (2011). Exudates dynamic detection in retinal fundus images based on the noise map distribution. In 19th European signal processing conference (pp. 46–50). Piscataway: IEEE

  39. Osareh, A., Mirmehdi, M., Thomas, B., & Markham, R. (2003). Automated identification of diabetic retinal exudates in digital colour images. British Journal of Ophthalmology, 87(10), 1220–1223.

    Article  Google Scholar 

  40. Gardner, G., Keating, D., Williamson, T., & Elliott, A. (1996). Automatic detection of diabetic retinopathy using an artificial neural network: A screening tool. British Journal of Ophthalmology, 80(11), 940–944.

    Article  Google Scholar 

  41. Phillips, R., Forrester, J., & Sharp, P. (1993). Automated detection and quantification of retinal exudates. Graefe’s archive for clinical and experimental ophthalmology, 231(2), 90–94.

    Article  Google Scholar 

  42. Jagoe, J., Blauth, C., Smith, P., Arnold, J., Taylor, K., & Wootton, R. (1990) Quantification of retinal damage during cardiopulmonary bypass: comparison of computer and human assessment. In IEE proceedings communications, speech and vision (vol. 137, pp. 17–175, vol. 3), IET.

  43. Pereira, C., Gonçalves, L., & Ferreira, M. (2015). Exudate segmentation in fundus images using an ant colony optimization approach. Information Sciences, 296, 14–24.

    Article  MathSciNet  Google Scholar 

  44. MESSIDOR: Digital Retinal Images. Retrieved from http://MESSIDOR.crihan.fr.

  45. Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J.-C., Meyer, F., et al. (2013). TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM, 34(2), 196–203.

    Article  Google Scholar 

  46. Kohler, T., Budai, A., Kraus, M. F., Odstrcilik, J., Michelson, G., & Hornegger, J. (2013) Automatic No-reference quality assessment for retinal fundus images using vessel segmentation. In IEEE 26th international symposium on computer-based medical systems (CBMS) (pp. 95–100). Piscataway: IEEE

  47. Osareh, A., Shadgar, B., & Markham, R. (2009). A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images. IEEE Transactions on Information Technology in Biomedicine, 13(4), 535–545.

    Article  Google Scholar 

  48. Buenaposada, J. M., & Baumela, L. (2001). Variations of grey world for face tracking. Image Processing and Communications, 7(3–4), 51–62.

    MATH  Google Scholar 

  49. Finlayson, G. D., Schiele, B., & Crowley, J. L. (1998). Comprehensive colour image normalization. In Computer vision—ECCV’98 (pp. 475–490). Berlin: Springer.

  50. Youssif, A. A., Ghalwash, A. Z., & Ghoneim, A. S. (2007). A comparative evaluation of preprocessing methods for automatic detection of retinal anatomy. In Proceedings of the fifth international conference on informatics and systems (INFOS 07) (vol. 2430)

  51. Goatman, K. A., Whitwam, A. D., Manivannan, A., Olson, J. A., & Sharp, P. F. (2003) Colour normalisation of retinal images. In Proceedings of medical image understanding and analysis (pp. 49–52), Citeseer

  52. Nagy, B., Antal, B., Harangi, B., & Hajdu, A. (2011) Ensemble-based exudate detection in color fundus images. In 7th international symposium on image and signal processing and analysis (ISPA) (pp. 700–703). Piscataway: IEEE

  53. Das, D. K., Ghosh, M., Pal, M., Maiti, A. K., & Chakraborty, C. (2013). Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron, 45, 97–106. doi:10.1016/j.micron.2012.11.002.

    Article  Google Scholar 

  54. Siddalingaswamy, P., & Prabhu, K. G. (2009). Automatic segmentation of blood vessels in colour retinal images using spatial gabor filter and multiscale analysis. In International conference on biomedical engineering (pp. 274–276). Berlin: Springer

  55. Chrástek, R., Wolf, M., Donath, K., Michelson, G., & Niemann, H. (2002). Optic disc segmentation in retinal images. In Bildverarbeitung für die Medizin (pp. 263–266). berlin: Springer.

  56. Lalonde, M., Beaulieu, M., & Gagnon, L. (2001). Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Transactions on Acoustics, Speech, and Signal Processing, 20(11), 1193–1200.

    Google Scholar 

  57. Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB. Upper Saddle River, NJ: Pearson Prentice Hall.

    Google Scholar 

  58. Chan, T. F., & Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266–277.

    Article  MATH  Google Scholar 

  59. Heinonen, P., & Neuvo, Y. (1987). FIR-median hybrid filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(6), 832–838.

    Article  MATH  Google Scholar 

  60. Rrnyi, A. (1961). On measures of entropy and information. In Fourth Berkeley symposium on mathematical statistics and probability (pp. 547–561)

  61. Kapur, J., Sahoo, P. K., & Wong, A. K. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 29(3), 273–285.

    Article  Google Scholar 

  62. Shanbhag, A. G. (1994). Utilization of information measure as a means of image thresholding. CVGIP: Graphical Models and Image Processing, 56(5), 414–419.

    Google Scholar 

  63. Yen, J.-C., Chang, F.-J., & Chang, S. (1995). A new criterion for automatic multilevel thresholding. IEEE Transactions on Image Processing, 4(3), 370–378.

    Article  MathSciNet  Google Scholar 

  64. Sahoo, P., Wilkins, C., & Yeager, J. (1997). Threshold selection using Renyi’s entropy. Pattern Recognition, 30(1), 71–84.

    Article  MATH  Google Scholar 

  65. Kauppi, T., Kalesnykiene, V., Kamarinen, J. K., Lensu, L., Sorri, I., Raninen, A., et al. (2007). DIARETDB1 diabetic retinopathy database and evaluation protocol. Medical Image Understanding and Analysis (pp. 61–65).

  66. Sopharak, A., Uyyanonvara, B., Barman, S., & Williamson, T. H. (2008). Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Computerized Medical Imaging and Graphics, 32(8), 720–727.

    Article  Google Scholar 

  67. Jaafar, H. F., Nandi, A. K., & Al-Nuaimy, W. (2010). Automated detection of exudates in retinal images using a split-and-merge algorithm. In 18th European Signal Processing Conference (pp. 1622–1626). Piscataway: IEEE

  68. Niemeijer, M., van Ginneken, B., Russell, S. R., Suttorp-Schulten, M. S., & Abramoff, M. D. (2007). Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investigative Ophthalmology & Visual Science, 48(5), 2260–2267.

    Article  Google Scholar 

  69. Garcia, M., Sanchez, C., Diez, A., Lopez, M., & Hornero, R. (2006). Detection of hard exudates based on neural networks as a diagnostic aid in the screening for diabetic retinopathy. Telemedicine in Future Health.

  70. Ravishankar, S., Jain, A., & Mittal, A. (2009). Automated feature extraction for early detection of diabetic retinopathy in fundus images. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 210–217). Piscataway: IEEE

  71. Soares, I., Castelo-Branco, M., & Pinheiro, A. M. (2012). Curvature detection and segmentation of retinal exudates. In EEE International Symposium on Biomedical Imaging (ISBI) (pp. 1719–1722). Piscataway: IEEE

  72. STARE Project. Retrieved from http://www.ces.clemson.edu/~ahoover/stare/.

  73. Foracchia, M., Grisan, E., & Ruggeri, A. (2004). Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Transactions on Medical Imaging, 23(10), 1189–1195.

    Article  Google Scholar 

  74. Osareh, A. (2004). Automated identification of diabetic retinal exudates and the optic disc. University of Bristol, Bristol.

  75. Osareh, A., Mirmehdi, M., Thomas, B., & Markham, R. (2001). Automatic recognition of exudative maculopathy using fuzzy c-means clustering and neural networks. In Proceedings of Medical Image Understanding Analysis Conference (vol. 3, pp. 49–52)

  76. Pun, T. (1980). A new method for grey level picture thresholding using the entropy of the histogram. Signal Processing, 2(3), 223–237.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are very grateful to STARE, DIARETDB1, MESSIDOR, e-ophtha, and Friedrich-Alexander University Erlangen-Nuremberg-Brno University of Technology program for their online databases. Authors acknowledge the Department of Biotechnology, Government of India, for providing financial support to carry out this research work under grant BT/PR14073/BID/07/320/2010 dt. 19-02-2013. The first author also acknowledges the Council of Scientific and Industrial Research for financial support (award no. 09/81(1223)/2014/EMR-I dt. 12-08-2014).

Author information

Authors and Affiliations

  1. School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur, India

    Maitreya Maity, Dev Kumar Das, Dhiraj Manohar Dhane & Chandan Chakraborty

  2. Susrut Eye Foundation & Research Center, Kolkata, India

    Anirudhha Maiti

Authors
  1. Maitreya Maity
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dev Kumar Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dhiraj Manohar Dhane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chandan Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anirudhha Maiti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chandan Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maity, M., Das, D.K., Dhane, D.M. et al. Fusion of Entropy-Based Thresholding and Active Contour Model for Detection of Exudate and Optic Disc in Color Fundus Images. J. Med. Biol. Eng. 36, 795–809 (2016). https://doi.org/10.1007/s40846-016-0193-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-016-0193-1

Keywords

Search

Navigation