Automated segmentation of optic disc using statistical region merging and morphological operations

Abstract

Accurate Optic Disc (OD) segmentation is vital in designing systems that aid the diagnosis and evaluation of early phases of retinal diseases. However, in many images, the OD boundary is ambiguous, which makes the automated OD segmentation process very challenging. A method to segment OD based on statistical region merging and morphological operations is proposed in this paper. The proposed method is tested on standard databases MESSIDOR, DIARETDB1, DIARETDB0, and DRIONS-DB. The average overlap ratios are found to be 91.35% for DIARETDB1 images, 88.80% for DRIONS-DB images, 86.60% for DIARETDB0 images and 89.68% for MESSIDOR images, with average accuracies of 99.68%, 99.89%, 99.69%, and 99.93% respectively. A comparison with alternative methods showed that the proposed algorithm in OD segmentation is better than existing ones.

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

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

References

  1. 1.

    Perez-Rovira A, Trucco E (2010) Contextual optic disc location in retinal fundus images. J Mod Opt 57(2):136–144

    Google Scholar 

  2. 2.

    Kausu T, Gopi VP, Wahid KA, Doma W, Niwas SI (2018) Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images. Biocybern Biomed Eng 38(2):329–341

    Google Scholar 

  3. 3.

    Fleming AD, Goatman KA, Philip S, Olson JA, Sharp PF (2006) Automatic detection of retinal anatomy to assist diabetic retinopathy screening. Phys Med Biol 52(2):331

    PubMed  Google Scholar 

  4. 4.

    Gopi VP, Anjali MS, Niwas SI (2017) PCA-based localization approach for segmentation of optic disc. Int J Comput Assist Radiol Surg 12(12):2195–2204

    PubMed  Google Scholar 

  5. 5.

    Hoover A, Goldbaum M (2003) Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 22(8):951–958

    PubMed  Google Scholar 

  6. 6.

    Kande GB, Subbaiah PV, Savithri TS (2008) Segmentation of exudates and optic disk in retinal images. In: Computer vision, graphics & image processing, 2008. ICVGIP’08. Sixth Indian conference on, IEEE, pp 535–542

  7. 7.

    Setiawan AW, Mengko TR, Santoso OS, Suksmono AB (2013) Color retinal image enhancement using CLAHE. In: ICT for Smart Society (ICISS), 2013 international conference on, IEEE, pp 1–3

  8. 8.

    Li H, Chutatape O (2001) Automatic location of optic disk in retinal images. In: Image processing, 2001. Proceedings. 2001 international conference on, IEEE, vol 2, pp 837–840

  9. 9.

    Abed S, Al-Roomi SA, Al-Shayeji M (2016) Effective optic disc detection method based on swarm intelligence techniques and novel pre-processing steps. Appl Soft Comput 49:146–163

    Google Scholar 

  10. 10.

    Walter T, Klein JC, 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 Trans Med Imaging 21(10):1236–1243

    PubMed  Google Scholar 

  11. 11.

    Seo J, Kim K, Kim J, Park K, Chung H (2004) Measurement of ocular torsion using digital fundus image. In: Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th annual international conference of the IEEE, IEEE, vol 1, pp 1711–1713

  12. 12.

    Stapor K, Świtonski A, Chrastek R, Michelson G (2004) Segmentation of fundus eye images using methods of mathematical morphology for glaucoma diagnosis. In: International conference on computational science, Springer, pp 41–48

  13. 13.

    Lupascu CA, Tegolo D, Di Rosa L (2008) Automated detection of optic disc location in retinal images. In: Computer-based medical systems, 2008. CBMS’08. 21st IEEE international symposium on, IEEE, pp 17–22

  14. 14.

    Hsiao HK, Liu CC, Yu CY, Kuo SW, Yu SS (2012) A novel optic disc detection scheme on retinal images. Expert Syst Appl 39(12):10600–10606

    Google Scholar 

  15. 15.

    Dias MA, Monteiro FC (2012) Optic disc detection using ant colony optimization. In: AIP conference proceedings, American Institute of Physics, vol 1479, pp 798–801

  16. 16.

    Morales S, Naranjo V, Angulo J, Alcañiz M (2013) Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Trans Med Imaging 32(4):786–796

    PubMed  Google Scholar 

  17. 17.

    Welfer D, Scharcanski J, Marinho DR (2013) A morphologic two-stage approach for automated optic disk detection in color eye fundus images. Pattern Recogn Lett 34(5):476–485

    Google Scholar 

  18. 18.

    Choukikar P, Patel AK, Mishra RS (2014) Segmenting the optic disc in retinal images using thresholding. Int J Comput Appl 94(11):6–10

    Google Scholar 

  19. 19.

    Salazar-Gonzalez AG, Kaba D, Li Y, Liu X (2014) Segmentation of the blood vessels and optic disk in retinal images. IEEE J Biomed Health Inform 18(6):1874–1886

    PubMed  Google Scholar 

  20. 20.

    Marin D, Gegundez-Arias ME, Suero A, Bravo JM (2015) Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput Methods Programs Biomed 118(2):173–185

    PubMed  Google Scholar 

  21. 21.

    Roychowdhury S, Koozekanani DD, Kuchinka SN, Parhi KK (2016) Optic disc boundary and vessel origin segmentation of fundus images. IEEE J Biomed Health Inform 20(6):1562–1574

    PubMed  Google Scholar 

  22. 22.

    Abdullah M, Fraz MM, Barman SA (2016) Localization and segmentation of optic disc in retinal images using Circular Hough transform and grow-cut algorithm. PeerJ 4:e2003

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    e Silva RRV, de Araujo FHD, dos Santos LMR, Veras RdMS, de Medeiros FNS (2016) Optic disc detection in retinal images using algorithms committee with weighted voting. IEEE Latin Am Trans 14(5):2446–2454

    Google Scholar 

  24. 24.

    Rahebi J, Hardalaç F (2016) A new approach to optic disc detection in human retinal images using the firefly algorithm. Med Biol Eng Comput 54(2–3):453–461

    PubMed  Google Scholar 

  25. 25.

    Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S (2017) Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 20:70–79

    Google Scholar 

  26. 26.

    Abdullah AS, Özok YE, Rahebi J (2018) A novel method for retinal optic disc detection using bat meta-heuristic algorithm. Med Biol Eng Comput 56(11):2015–2024

    PubMed  Google Scholar 

  27. 27.

    Aguirre-Ramos H, Avina-Cervantes JG, Cruz-Aceves I, Ruiz-Pinales J, Ledesma S (2018) Blood vessel segmentation in retinal fundus images using gabor filters, fractional derivatives, and expectation maximization. Appl Math Comput 339:568–587

    Google Scholar 

  28. 28.

    Jadhav AS, Patil PB (2016) Detection of optic disc from retinal images using wavelet transform. In: 2016 international conference on signal processing, communication, power and embedded system (SCOPES), pp 178–181. https://doi.org/10.1109/SCOPES.2016.7955754

  29. 29.

    Gui B, Shuai RJ, Chen P (2018) Optic disc localization algorithm based on improved corner detection. Procedia Comput Sci 131:311–319

    Google Scholar 

  30. 30.

    Fan Z, Rong Y, Cai X, Lu J, Li W, Lin H, Chen X (2017) Optic disk detection in fundus image based on structured learning. IEEE J Biomed Health Inform 22(1):224–234

    PubMed  Google Scholar 

  31. 31.

    Nergiz M, Akın M, Yıldız A, Takeş Ö (2018) Automated fuzzy optic disc detection algorithm using branching of vessels and color properties in fundus images. Biocybern Biomed Eng 38(4):850–867

    Google Scholar 

  32. 32.

    Xue LY, Lin JW, Cao XR, Zheng SH, Yu L (2018) Optic disk detection and segmentation for retinal images using saliency model based on clustering. J Comput 29(5):66–79

    Google Scholar 

  33. 33.

    Pruthi J, Arora S, Khanna K (2018) Metaheuristic techniques for detection of optic disc in retinal fundus images. 3D Research 9(4):47

    Google Scholar 

  34. 34.

    Ünver HM, Kökver Y, Duman E, Erdem OA (2019) Statistical edge detection and circular hough transform for optic disk localization. Appl Sci 9(2):350

    Google Scholar 

  35. 35.

    Maninis KK, Pont-Tuset J, Arbeláez P, Van Gool L (2016) Deep retinal image understanding. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 140–148

  36. 36.

    Shelhamer E, Long J, Darrell T (2016) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39:1–1. https://doi.org/10.1109/TPAMI.2016.2572683

    Google Scholar 

  37. 37.

    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556

  38. 38.

    Sevastopolsky A (2017) Optic disc and cup segmentation methods for glaucoma detection with modification of u-net convolutional neural network. Pattern Recognit Image Anal 27:618. https://doi.org/10.1134/S1054661817030269

    Google Scholar 

  39. 39.

    Liu S, Hong J, Lu X, Jia X, Lin Z, Zhou Y, Liu Y, Zhang H (2019) Joint optic disc and cup segmentation using semi-supervised conditional GANs. Comput Biol Med 115:10385. https://doi.org/10.1016/j.compbiomed.2019.103485

    Google Scholar 

  40. 40.

    Fu H, Cheng J, Xu Y, Wong D, Liu J, Cao X (2018) Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Medical Imaging. https://doi.org/10.1109/TMI.2018.2791488

  41. 41.

    Liu Q, Hong X, Li S, Chen Z, Zhao G, Zou B (2019) A spatial-aware joint optic disc and cup segmentation method. Neurocomputing 359:285. https://doi.org/10.1016/j.neucom.2019.05.039

    Google Scholar 

  42. 42.

    Wang L, Liu H, Lu Y, Chen H, Zhang J, Pu J (2019) A coarse-to-fine deep learning framework for optic disc segmentation in fundus images. Biomed Signal Process Control 51:82–89. https://doi.org/10.1016/j.bspc.2019.01.022

    Google Scholar 

  43. 43.

    Yu S, Xiao D, Frost S, Kanagasingam Y (2019) Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput Med Imaging Graphics 74:61. https://doi.org/10.1016/j.compmedimag.2019.02.005

    Google Scholar 

  44. 44.

    Suter D (2008) Object detection by global contour shape. Pattern Recogn 41(02):3736–3748

    Google Scholar 

  45. 45.

    Celebi ME, Kingravi HA, Iyatomi H, Lee J, Aslandogan YA, Stoecker WV, Moss R, Malters JM, Marghoob AA (2007) Fast and accurate border detection in dermoscopy images using statistical region merging. In: Pluim JPW, Reinhardt JM (eds) Medical imaging 2007: image processing, international society for optics and photonics, SPIE, vol 6512, pp 1297–1306. https://doi.org/10.1117/12.709073, URL https://doi.org/10.1117/12.709073

  46. 46.

    Celebi ME, Kingravi H, Iyatomi H, Aslandogan Y, Stoecker W, Moss R, Malters J, Grichnik J, Marghoob A, Rabinovitz H, Menzies S (2008) Border detection in dermoscopy images using statistical region merging. Skin Res Technol 14:347–53. https://doi.org/10.1111/j.1600-0846.2008.00301.x

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Bajger M, Ma F, Williams S, Bottema M (2010) Mammographic mass detection with statistical region merging. In: 2010 international conference on digital image computing: Techniques and applications, IEEE, pp 27–32

  48. 48.

    Lee GN, Bajger M, Caon M (2013) 3d segmentation for multi-organs in CT images. Electron Lett Comput Vis Image Anal 12(02):13–27

    Google Scholar 

  49. 49.

    Lee G, Bajger M, Caon M (2012) Multi-organ segmentation of CT images using statistical region merging. In: Proceedings of the 9th IASTED international conference on biomedical engineering, BioMed 2012 https://doi.org/10.2316/P.2012.764-052

  50. 50.

    Bajger M, Lee G, Caon M (2012) Full-body ct segmentation using 3d extension of two graphbased methods: a feasibility study. In: Signal processing, pattern recognition and applications (SPPRA 2012): Proc. of the iasted international conference, pp 43–50

  51. 51.

    Wong A, Scharcanski J (2011) Fisher-tippett region-merging approach to transrectal ultrasound prostate lesion segmentation. IEEE Trans Inf Technol Biomed 15:900–7. https://doi.org/10.1109/TITB.FGAN2011.2163724

    PubMed  Google Scholar 

  52. 52.

    Nock R, Nielsen F (2004) Statistical region merging. IEEE Trans Pattern Anal Mach Intell 26(11):1452–1458

    PubMed  Google Scholar 

  53. 53.

    Gonzalez Rafael C, Woods Richard E (2002) Digital image processing, 2nd edn. Prentice hall, Upper Saddle River

    Google Scholar 

  54. 54.

    Lee GN, Bajger M, Caon M (2012) Multi-organ segmentation of ct images using statistical region merging. In: Biomedical engineering (BioMed 2012): Proc of the IASTED international conference 2007, pp 199–206

  55. 55.

    Mirmehdi M, Petrou M (2000) Segmentation of color textures. IEEE Trans Pattern Anal Mach Intell 22(2):142–159

    Google Scholar 

  56. 56.

    Palm C, Keysers D, Lehmann T, Spitzer K (2000) Gabor filtering of complex hue/saturation images for color texture classification. In: Proc. JCIS, Citeseer, pp 45–49

  57. 57.

    Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A et al (2014) Feedback on a publicly distributed image database: the MESSIDOR database. Image Anal Stereol 33(3):231–234. https://doi.org/10.5566/ias.1155

    Google Scholar 

  58. 58.

    Kälviäinen R, Uusitalo H (2007) Diaretdb1 diabetic retinopathy database and evaluation protocol. In: Medical image understanding and analysis, Citeseer, vol 2007, p 61

  59. 59.

    Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Uusitalo H, Kälviäinen H, Pietilä J (2006) Diaretdb0: evaluation database and methodology for diabetic retinopathy algorithms. Mach Vis Pattern Recognit Res Group 73:1–17

    Google Scholar 

  60. 60.

    Carmona EJ, Rincón M, García-Feijoó J, Martínez-de-la Casa JM (2008) Identification of the optic nerve head with genetic algorithms. Artif Intell Med 43(3):243–259

    PubMed  Google Scholar 

  61. 61.

    Forsyth DA, Ponce J (2002) Computer vision: a modern approach. Prentice Hall Professional Technical Reference

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Varun P. Gopi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study, formal consent is not required.

Informed consent

This article does not contain any studies with human participants or animals performed by any of the authors.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nija, K.S., Anupama, C.P., Gopi, V.P. et al. Automated segmentation of optic disc using statistical region merging and morphological operations. Phys Eng Sci Med (2020). https://doi.org/10.1007/s13246-020-00883-2

Download citation

Keywords

  • Optic disc
  • Statistical region merging
  • Diabetic retinopathy
  • Morphological operations
  • OD segmentation