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Optic disc analysis in retinal fundus using L2 norm of contourlet subbands, superimposed edges, and morphological filling

  • 1210: Computer Vision for Clinical Images
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Abstract

Optic disc (OD) analysis is an important stage in detecting retinal diseases and existing approaches are not suitable for analyzing multiple examinations in a single process. This paper proposes a new unified method to detect (i) OD center, (ii) OD boundary, and (iii) enhancement of vasculature within the OD region in a single algorithm. This paper presents a unique strategy, which involves L2 norm of contourlet subbands of retinal images to locate the OD center. A novel OD boundary tracing approach that integrates morphological operations, and segmentation region growing techniques, with the aid of the OD center as a seed point, is presented. Among the two novel OD vasculature enhancement techniques, one of which involves image sharpening to improve the local contrast followed by histogram equalization to increase the overall contrast of the fundus image. Edge superimposing with the flood fill technique is another approach used for vasculature enhancement. The algorithm is tested on DRIVE (40), STARE (81), MESSIDOR (1200), E-ophtha (87), Diaretdb1 (89) images, out of which 40, 77, 1182, 87, and 87 images are detected correctly. The Figure of Merit (FOM) is used to confirm the correctness of the boundary tracking performance (97.15%). EME, SSIM, and other parameters are used to assess the efficiency of the vasculature enhancement technique.

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References

  1. Abràmoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 3:169–208

    Article  Google Scholar 

  2. Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Krishna Rao A, Hagiwara Y, Chua CK, Laude A (2017) Automated diabetic macular edema (DME) grading system using DWT, DCT features and maculopathy index. Comput Biol Med 84:59–68

    Article  Google Scholar 

  3. Agurto C, Yu H, Murray V, Pattichis MS, Barriga S, Bauman W, Soliz P (2012) Detection of neovascularization in the optic disc using an AM-FM representation, granulometry, and vessel segmentation. 2012 Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2012

  4. Agurto C, Yu H, Murray V, Pattichis MS, Nemeth S, Barriga S, Soliz P (2015) Detection of neovascularization in the optic disc to detect abnormal vasculature in the optic disc. Comput Med Imaging Graph 43:137–149

  5. Al-Diri B, Hunter A, Steel D (2009) An active contour model for segmenting and measuring retinal vessels. IEEE Trans Med Imaging 28(9):1488–1497

    Article  Google Scholar 

  6. Anand S, Gayathri S (2015) Mammogram image enhancement by two stage adaptive histogram equalization. Optik Int J Light Electron Opt 126(21):3150–3152

    Article  Google Scholar 

  7. Anand S, Shantha Selvakumari R (2013) Sharpening enhancement of computed tomography (CT) images using hyperbolic secant square filter. Optik Int J Light Electron Optics 124(5):2121–2124

    Article  Google Scholar 

  8. Aquino A, Gegúndez-Arias ME, Marín D (2010) Detecting the optic disc boundary in digital fundus images using morphological, ED, and feature extraction techniques. IEEE Trans Med Imaging 29(11):1860–1869

    Article  Google Scholar 

  9. Chakravarty A, Sivaswamy J (2017) Joint optic disc and cup boundary extraction from monocular fundus images. Comput Methods Programs Biomed 147:51–61

    Article  Google Scholar 

  10. Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269

    Article  Google Scholar 

  11. Cheng J, Li Z, Zaiwang Gu, Huazhu Fu, Wong DWK, Liu J (2018) Structure-preserving guided retinal image filtering and its application for optic disc analysis. IEEE Trans Med Imaging 37(11):2536–2546

    Article  Google Scholar 

  12. Decencière E et al (2013) TeleOphta: machine learning and image processing methods for teleophthalmology. Irbm 34(2):296–203

    Article  Google Scholar 

  13. Diaretdb M (2009) DiaRetDB1: diabetic retinopathy database and evaluation protocol

  14. Fan Z, Rong Y, Cai X, Jiewei Lu, Li W, Lin H, Chen X (2018) Optic disk detection in fundus image based on structured learning. IEEE J Biomed Health Inform 22(1):224–234

    Article  Google Scholar 

  15. Foracchia M, Grisan E, Ruggeri A (2004) Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans Med Imaging 23(10):1189–1195

    Article  Google Scholar 

  16. Gegundez-Arias ME, Marin D, Bravo JM, Suero A (2013) Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques. Comput Med Imaging Graph 37(5–6):386–393

    Article  Google Scholar 

  17. Gegundez-Arias ME, Marin D, Ponte B, Alvarez F, Garrido J, Ortega C, Vasallo MJ, Bravo JM (2017) A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis. Comput Biol Med 88:100–109

    Article  Google Scholar 

  18. Goatman KA, Fleming AD, Philip S, Williams GJ, Olson JA, Sharp PF (2011) Detection of new vessels on the optic disc using retinal photographs. IEEE Trans Med Imaging 30(4):972–979

    Article  Google Scholar 

  19. Harangi B, Hajdu A (2015) Detection of the optic disc in fundus images by combining probability models. Comput Biol Med 65:10–24

    Article  Google Scholar 

  20. Hoover AD, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210

    Article  Google Scholar 

  21. Javidi M, Pourreza H-R, Harati A (2017) Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation. Comput Methods Programs Biomed 139:93–108

    Article  Google Scholar 

  22. Jiang G, Lin SCF, Wong CY, Rahman MA, Ren TR, Kwok N, Shi H, Yu Y-H, Wu T (2015) Color image enhancement with brightness preservation using a histogram specification approach. Optik 126(24):5656–5664

    Article  Google Scholar 

  23. Joshi GD, Sivaswamy J, Krishnadas SR (2011) Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans Med Imaging 30(6):1192–1205

    Article  Google Scholar 

  24. Kamble R, Kokare M, Deshmukh G, Hussin FA, Mériaudeau F (2017) Localization of optic disc and fovea in retinal images using intensity based line scanning analysis. Comput Biol Med 87:382–396

    Article  Google Scholar 

  25. Kao E-F, Lin P-C, Chou M-C, Jaw T-S, Liu G-C (2014) Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template. Comput Methods Programs Biomed 117(2):92–103

    Article  Google Scholar 

  26. Kar SS, Maity SP (2016) Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means. Comput Biol Med 70:174–189

    Article  Google Scholar 

  27. Koh JEW, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Vinitha Sree S, Bhandary SV et al (2017) Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies. Comput Biol Med 84:89–97

    Article  Google Scholar 

  28. Lalonde M, Beaulieu M, Gagnon L (2001) Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans Med Imaging 20(11):1193–1200

    Article  Google Scholar 

  29. Lupascu CA, Tegolo D, Trucco E (2010) FABC: retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed 14(5):1267–1274

    Article  Google Scholar 

  30. 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

    Article  Google Scholar 

  31. Medhi JP, Dandapat S (2016) An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images. Comput Biol Med 74:30–44

    Article  Google Scholar 

  32. Mendonca AM, Sousa A, Mendonça L, Campilho A (2013) Automatic localization of the optic disc by combining vascular and intensity information. Comput Med Imaging Graph 37(5–6):409–417

    Article  Google Scholar 

  33. Miri MS, Mahloojifar A (2011) Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans Biomed Eng 58(5):1183–1192

    Article  Google Scholar 

  34. Mitra A, Roy S, Roy S, Setua SK (2018) Enhancement and restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of cataract. Comput Methods Programs Biomed 156:169–178

    Article  Google Scholar 

  35. Muramatsu C, Nakagawa T, Sawada A, Hatanaka Y, Hara T, Yamamoto T, Fujita H (2009) Determination of cup-to-disc ratio of optical nerve head for diagnosis of glaucoma on stereo retinal fundus image pairs. In Medical Imaging 2009: Computer-Aided Diagnosis, vol. 7260, p. 72603L. International Society for Optics and Photonics, 2009

  36. Narasimha-Iyer H, Can A, Roysam B, Tanenbaum HL, Majerovics A (2007) Integrated analysis of vascular and nonvascular changes from color retinal fundus image sequences. IEEE Trans Biomed Eng 54(8):1436–1445

    Article  Google Scholar 

  37. Palomera-Pérez MA, Martinez-Perez ME, Benítez-Pérez H, Ortega-Arjona JL (2010) Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Trans Inf Technol Biomed 14(2):500–506

    Article  Google Scholar 

  38. Roy PK, Bhuiyan A, Lee K, Wong TY, Ramamohanarao K (2016) A novel computer aided quantification method of focal arteriolar narrowing using colour retinal image. Comput Biol Med 74:18–29

    Article  Google Scholar 

  39. Singh A, Dutta MK, Partha Sarathi M, Uher V, Burget R (2016) Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Programs Biomed 124:108–120

    Article  Google Scholar 

  40. Soares I, Castelo-Branco M, Pinheiro AMG (2016) Optic disc localization in retinal images based on cumulative sum fields. IEEE J Biomed Health Inf 20(2):574–585

    Article  Google Scholar 

  41. Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509

    Article  Google Scholar 

  42. Swaminathan A, Ramapackiam SSK, Thiraviam T, Selvaraj J (2013) Contourlet transform-based sharpening enhancement of retinal images and vessel extraction application. Biomed Tech/Biomed Eng 58(1):87–96

    Google Scholar 

  43. Tan N-M, Xu Y, Boon Goh W, Liu J (2015) Robust multi-scale superpixel classification for optic cup localization. Comput Med Imaging Graph 40:182–193

    Article  Google Scholar 

  44. Tramontan L, Poletti E, Fiorin D, Ruggeri A (2011) A web-based system for the quantitative and reproducible assessment of clinical indexes from the retinal vasculature. IEEE Trans Biomed Eng 58(3):818

    Article  Google Scholar 

  45. Vostatek P, Claridge E, Uusitalo H, Hauta-Kasari M, Fält P, Lensu L (2017) Performance comparison of publicly available retinal blood vessel segmentation methods. Comput Med Imaging Graph 55:2–12

    Article  Google Scholar 

  46. Wong CY, Liu S, Liu SC, Rahman MA, Lin SCF, Jiang G, Kwok N, Shi H (2016) Image contrast enhancement using histogram equalization with maximum intensity coverage. J Modern Opt 63(16):1618–1629

    Article  MathSciNet  MATH  Google Scholar 

  47. **ong Li, Li H (2016) An approach to locate optic disc in retinal images with pathological changes. Comput Med Imaging Graph 47:40–50

    Article  Google Scholar 

  48. **ong Li, Li H, Liang Xu (2017) An enhancement method for color retinal images based on image formation model. Comput Methods Programs Biomed 143:137–150

    Article  Google Scholar 

  49. Yazdanpanah A, Hamarneh G, Smith BR, Sarunic MV (2011) Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach. IEEE Trans Med Imaging 30(2):484–496

    Article  Google Scholar 

  50. Youssif AA-H-R, Ghalwash AZ, Ghoneim AASA-R (2008) Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 27(1):11–18

    Article  Google Scholar 

  51. Zhang D, Zhao Y (2016) Novel accurate and fast optic disc detection in retinal images with vessel distribution and directional characteristics. IEEE J Biomed Health Inf 20(1):333–342

    Article  Google Scholar 

  52. Zhou M, ** K, Wang S, Ye J, Qian D (2018) Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Trans Biomed Eng 65(3):521–527

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, 626005, India

    S. Anand

  2. CTS, Chennai, India

    S. Gayathri

  3. Department of ECE, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, 626005, India

    G. Sangeethapriya

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  1. S. Anand
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  2. S. Gayathri
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Correspondence to S. Anand.

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Highlights

• L2 Norm of Contourlet subbands of several levels used to detect the OD.

• The OD boundary is traced by engaging the morphological region growing techniques.

• Sharpening, followed by adaptive histogram equalization, was used to enhance the visibility of the vessel structure.

• Contourlet based edge information together with morphological operators is used for vessel enhancement.

• OD detection, boundary tracking, and enhancement by a chain of imaging algorithms.

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Anand, S., Gayathri, S. & Sangeethapriya, G. Optic disc analysis in retinal fundus using L2 norm of contourlet subbands, superimposed edges, and morphological filling. Multimed Tools Appl 81, 36129–36152 (2022). https://doi.org/10.1007/s11042-021-11569-6

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  • DOI: https://doi.org/10.1007/s11042-021-11569-6

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