SpringerLink
Log in

Exudate and drusen classification in retinal images using bagged colour vector angles and inter colour local binary patterns

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The presence of exudates is one of the most significant signs of Diabetic retinopathy (DR) whereas; white or tiny yellow deposits known as drusen mostly identify age-related macular degeneration (AMD). Exudates and drusen may share a similar appearance; hence discriminating them is of extreme importance in enhancing automated AMD and DR diagnosis. Fortunately, diagnosing these diseases in their early stages is extremely useful for effective treatment since they are usually treatable. The goal of this research is to develop an automated tool that helps the pathologist diagnose the type of disease correctly and distinguish between DR, AMD, and normal fundus images through accurate classification of exudates and drusen lesions. In this paper, an automatic retinal diagnosis system that combines different texture and colour features is proposed. New textural and colour features are used in a bag-of-features approach for efficient and accurate detection. A codebook is generated using a bagged combination of inter colour local binary pattern (ICLBP) and colour vector angles (CVA) features to exploit textural and colour information for efficient and accurate classification. Intensive experiments show that the proposed dictionary learning-based system can capture the variety of structures and patterns in retinal fundus images and produce discriminant descriptors for classification. Using an SVM classifier with the obtained bagged combination of the proposed ICLBP and CVA features, the system has been shown to offer high classification performance. The experimental performance has been obtained with a dataset of 798 retinal images collected from various standard datasets, namely: DIARETDB0, DIARETDB1, HEI-MED, STARE, and MESSIDOR. All experiments were conducted with 10-fold cross validation using the classification accuracy, sensitivity, specificity, and area under curve. Correct classification is reported with an average sensitivity of 98.37%, specificity of 99.64% and accuracy of 99.67% and an overall average area under the curve of 0.983%. This represents the best performance achieved so far when compared to existing state-of-the-art systems for the diagnosis of retinal disease with drusen and exudates being the key characteristics in fundus image classification.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The authors confirm that all data underlying the findings are fully available without restriction. All data are included within manuscript (Messidor [https://doi.org/10.5566/ias.1155] and Hei-Med [https://doi.org/10.1016/j.media.2011.07.004]). The supplementary data [Diaretdb0, Diaretdb1, Stare] associated with this article can be found, in the online version, at [https://www.it.lut.fi/project/imageret/diaretdb0/, https://www.it.lut.fi/project/imageret/diaretdb1/index.html, https://cecas.clemson.edu/~ahoover/stare/].

References

  1. Zhou B, Lu Y, Hajifathalian K, Bentham J, Di Cesare M, Danaei G, Bixby H, Cowan MJ, Ali MK, Taddei C, et al (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4\(\cdot \) 4 million participants The lancet 387(10027):1513–1530

  2. Kanski JJ, Bowling B (2011) Clinical ophthalmology: a systematic approach 7 edn, Elsevier Health Sciences, 382

  3. Aggarwal K, Mijwil MM, Al-Mistarehi A-H, Alomari S, Gok M, Zein Alaabdin AM, Abdulrhman SH (2022) Has the future started? the current growth of artificial intelligence, machine learning, and deep learning. Iraqi J Comput Sc Math 3:115–123

    Google Scholar 

  4. Srinivasu PN, JayaLakshmi G, Jhaveri RH, Phani Praveen S (2022) Ambient assistive living for monitoring the physical activity of diabetic adults through body area networks. Mob Inf Syst, 3169927

  5. Antal B, Hajdu A (2014) An ensemble-based system for automatic screening of diabetic retinopathy. Knowl-Based Syst 60:20–27

    Article  Google Scholar 

  6. Sánchez CI, Hornero R, López MI, Aboy M, Poza J, Abásolo D, (2008) A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. Med Eng Phys 30(3):350–357

  7. Nugroho HA, Oktoeberza KW, Adji TB, Najamuddin F (2015) Detection of Exudates on Color Fundus Images using. Texture Based Feature Extraction 6(2):04

    Google Scholar 

  8. Khalid S, Akram MU, Khalil T(2017) Hybrid textural feature set based automated diagnosis system for age related macular degeneration using fundus images In: IEEE International conference on communication, computing and digital systems (C-CODE), pp 390–395

  9. Karnon J, Czoski-Murray C, Smith K, Brand C, Chakravarthy U, Davis S, Bansback N, Beverley C, Bird A, Harding S (2008) A preliminary model-based assessment of the cost-utility of a screening programme for early age-related macular degeneration

  10. Nagi D, Gosden C, Walton C, Winocour P, Turner B, Williams R, James J, Holt R (2009) A national survey of the current state of screening services for diabetic retinopathy: ABCD-Diabetes UK survey of specialist diabetes services 2006 Diabetic medicine. Diabetic medicine 26(12):1301–1305

    Article  Google Scholar 

  11. Osareh A, Shadgar B, Markham R (2009) A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images. IEEE Trans Inf Technol Biomed 13(4):535–545

    Article  Google Scholar 

  12. Hassan T, Akram MU, Werghi N (2020) Evaluation of Deep Segmentation Models for the Extraction of Retinal Lesions from Multi-modal Retinal Images

  13. Afrin R, Shill PC (2019) Automatic lesions detection and classification of diabetic retinopathy using fuzzy logic In: 2019 International conference on robotics, electrical and signal processing techniques (ICREST), IEEE, pp 527–532

  14. Stolte S, Fang R (2020) A survey on medical image analysis in diabetic retinopathy. Med Image Anal 64:101742

    Article  Google Scholar 

  15. Kaur J, Mittal D, Singla R (2022) Diabetic retinopathy diagnosis through computer-aided fundus image analysis: a review. Archives Comput Methods Eng 29(3):1673–1711

    Article  Google Scholar 

  16. Akram MU, Khalid S, Khan SA (2013) Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit 46(1):107–116

    Article  Google Scholar 

  17. Sidibé ISD, Mériaudeau F (2015) Discrimination of retinal images containing bright lesions using sparse coded features and svm. Comput Biol Med 62:175–184

    Article  Google Scholar 

  18. Omar M, Hossain A, Zhang L, Shum H (2014) An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations In: The 8th international conference on software, knowledge, information management and applications (SKIMA 2014), IEEE, pp 1–5

  19. Grinsven MJ, Chakravarty A, Sivaswamy J, Theelen T, Ginneken B, Sánchez CI (2013) A bag of words approach for discriminating between retinal images containing exudates or drusen In: 2013 IEEE 10th international symposium on biomedical imaging, IEEE, pp 1444–1447

  20. Niemeijer M, Ginneken B, Russell SR, Suttorp-Schulten MS, Abramoff MD (2007) Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investig Ophthalmol Vis Sci 48(5):2260–2267

    Article  Google Scholar 

  21. Fleming AD, Philip S, Goatman KA, Williams GJ, Olson JA, Sharp PF (2007) Automated detection of exudates for diabetic retinopathy screening. Phys Med Biol 52(24):7385

    Article  Google Scholar 

  22. Chaum E, Karnowski TP, Govindasamy VP, Abdelrahman M, Tobin KW (2008) Automated diagnosis of retinopathy by content-based image retrieval. Retina 28(10):1463–1477

    Article  Google Scholar 

  23. Deepak KS, Sivaswamy J (2011) Automatic assessment of macular edema from color retinal images. IEEE Trans Med Imaging 31(3):766–776

    Article  Google Scholar 

  24. Sánchez CI, Niemeijer M, Išgum I, Dumitrescu A, Suttorp-Schulten MS, Abrámoff MD, Ginneken B, (2012) Contextual computer-aided detection: improving bright lesion detection in retinal images and coronary calcification identification in CT scans. Med Image Anal 16(1):50–62

  25. Grinsven MJ, Theelen T, Witkamp L, Heijden J, Ven JP, Hoyng CB, Ginneken B, Sánchez CI (2016) Automatic differentiation of color fundus images containing drusen or exudates using a contextual spatial pyramid approach. Biomed Opt Express 7(3):709–725

    Article  Google Scholar 

  26. Niemeijer M, Abrámoff MD, Van Ginneken B (2009) Fast detection of the optic disc and fovea in color fundus photographs. Med Image Anal 13(6):859–870

    Article  Google Scholar 

  27. Hunter A, Lowell J, Owens J, Kennedy L, Steele D (2000) Quantification of diabetic retinopathy using neural networks and sensitivity analysis. In: Artificial neural networks in medicine and biology: proceedings of the ANNIMAB-1 conference, Göteborg, Sweden, 13–16 May 2000, Springer, pp 81–86

  28. Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Garg S, Tobin KW, Chaum E (2012) Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med Image Anal 16(1):216–226

    Article  Google Scholar 

  29. Hari V, Raj VJ, Gopikakumari R (2017) Quadratic filter for the enhancement of edges in retinal images for the efficient detection and localization of diabetic retinopathy. Pattern Anal Applic 20(1):145–165

    Article  MathSciNet  Google Scholar 

  30. Omar M, Khelifi F, Tahir MA (2016) Detection and classification of retinal fundus images exudates using region based multiscale lbp texture approach. In: 2016 International conference on control, decision and information technologies (CoDIT), IEEE, pp 227–232

  31. Akram UM, Khan SA (2012) Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy. J Med Syst 36(5):3151–3162

    Article  Google Scholar 

  32. Omar MA, Tahir MA, Khelifi F (2017) Multi-label learning model for improving retinal image classification in diabetic retinopathy. In: 2017 4th international conference on control, decision and information technologies (CoDIT), IEEE, pp 0202–0207

  33. Li Z, Guo C, Nie D, Lin D, Cui T, Zhu Y, Chen C, Zhao L, Zhang X, Dongye M (2021) Automated detection of retinal exudates and drusen in ultra-widefield fundus images based on deep learning, 1–6

  34. Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274–282

    Article  Google Scholar 

  35. Butt MM, Iskandar DNFA, Abdelhamid SE, Latif G, Alghazo R (2022) Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features. Diagnostics 7:1607

    Article  Google Scholar 

  36. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intel 24(7):971–987

    Article  Google Scholar 

  37. Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intel 6:915–928

    Article  Google Scholar 

  38. Tang Z, Dai Y, Zhang X, Huang L, Yang F (2013) Robust image hashing via colour vector angles and discrete wavelet transform. IET Image Process 8(3):142–149

    Article  Google Scholar 

  39. Dony R, Wesolkowski S (1999) Edge detection on color images using rgb vector angles. In: Engineering solutions for the next millennium 1999 IEEE canadian conference on electrical and computer engineering (Cat No 99TH8411), vol 2. IEEE, pp 687–692

  40. Zhang J, Marszałek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73(2):213–238

    Article  Google Scholar 

  41. Kauppi T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Uusitalo H, Kälviäinen H, Pietilä J (2006) DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms

  42. Kälviäinen RVJPH, Uusitalo H (2007) DIARETDB1 diabetic retinopathy database and evaluation protocol 61

  43. 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

    Article  Google Scholar 

  44. Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, Charton B, Klein J-C (2014) Feedback on a publicly distributed image database: the messidor database. Image Anal Stereology 33(3):231–234

    Article  Google Scholar 

  45. Turan C, Lam K-M (2018) Histogram-based local descriptors for facial expression recognition (fer): a comprehensive study. J Vis Commun Image Represent 55:331–341

    Article  Google Scholar 

  46. Li H, Qiao Q (2019) Localisation of insulator strings’ images based on colour filtering and texture matching (16):2790–2793

Download references

Author information

Authors and Affiliations

  1. Dept. of Creative, Digital, and Computing, Buckinghamshire College Group, Aylesbury, UK

    Mohamed Albashir Omar

  2. Department of Computer and Information Sciences, Northumbria University, Newcastle, UK

    Fouad Khelifi

  3. School of Computer Sciences, National University of Computer & Emerging Sciences, Karachi-Campus, Islamabad, Pakistan

    Muhammad Atif Tahir

Authors
  1. Mohamed Albashir Omar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fouad Khelifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Atif Tahir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Albashir Omar.

Ethics declarations

Conflicts of interest

All authors declare that there are no conflicts of interest in this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Omar, M.A., Khelifi, F. & Tahir, M.A. Exudate and drusen classification in retinal images using bagged colour vector angles and inter colour local binary patterns. Multimed Tools Appl 83, 51809–51833 (2024). https://doi.org/10.1007/s11042-023-17169-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17169-w

Keywords

Search

Navigation