Skip to main content
SpringerLink
Log in

A Review: Hemorrhage Detection Methodologies on the Retinal Fundus Image

  • Conference paper
  • First Online:
Applications of Artificial Intelligence and Machine Learning

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 778))

  • 1445 Accesses

Abstract

Diabetic retinopathy (DR) is a microvascular symptom where retina is affected by fluid leaks of the fragile blood vessels. Clinically, retinal Hemorrhages are one of the earliest indications of diabetic retinopathy disease. In this contrast, the Hemorrhage count is used to indicate the severity of this disease. The early detection of retinal Hemorrhages obviously prevents the incurable blindness of the DR patients. But, retinal Hemorrhage detection is still a challenging task. Highly reliable, accurate, platform independent retinal Hemorrhage detection method is still an open field. In this research article, we have reviewed the principal methodologies which are used to diagnose the retinal Hemorrhages under the diabetic retinopathy screening operations. This review article helps the researchers to develop a high quality retinal Hemorrhage screening method in future.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shah P, Mishra D, Shanmugam M, Doshi B, Jayaraj H, Ramanjulu R (2020) Validation of deep convolutional neural network-based algorithm for detection of diabetic retinopathy—artificial intelligence versus clinician for screening. Indian J Ophthalmol 68(2):398–405

    Article  Google Scholar 

  2. Datta N, Dutta H, Majumder K, Chatterjee S, Wasim N (2019) An improved method for automated identification of hard exudates in diabetic retinopathy disease. IETE J Res. https://doi.org/10.1080/03772063.2019.1618206

    Article  Google Scholar 

  3. Wiseng K, Hiransakolwong N, Pothirut E (2013) Automatic detection of retinal exudates using a support vector machine. Appl Med Inform 32(1):32–42

    Google Scholar 

  4. Kade M (2013) A survey of automatic techniques for retinal diseases identification in diabetic retinopathy. Int J Adv Res Technol 2:199–216

    Google Scholar 

  5. Datta N, Dutta H, Majumder K (2016) Brightness preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease. J Med Imaging 3(1):014502. https://doi.org/10.1117/1.JMI.3.1.014502

    Article  Google Scholar 

  6. Baint A, Andras H (2016) An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng 59(6):1720–1726

    Google Scholar 

  7. Garcia M, Lopez MI, Alvarez D, Hornero R (2010) Assessment of four neural network based classifiers to automatically detect red lesions in retinal images. Med Eng Phys 32(10):1085–1093

    Article  Google Scholar 

  8. Marino C, Ares E, Penedo M, Ortega M, Barreira N, Gomezulla F (2008) Automated three stage red lesions detection in digital color fundus images. WSEAS Trans Comput 7(4):207–215

    Google Scholar 

  9. Datta N, Dutta H, Majumder K (2016) An effective contrast enhancement method for identification of microaneurysms at early stage. IETE J Res. https://doi.org/10.1080/03772063.2015.1136573

  10. Esmaeili M, Rabbani H, Dehnavi A, Dehghani A (2010) A new curvelet transform based method for extraction of red lesions in digital color retinal images. In: Proceedings of the IEEE international conference 2010, ICIP. IEEE, Hong Kong, pp 4093–4096

    Google Scholar 

  11. Kande G, Tirumala S, Subbaiah P, Tagore M (2009) Detection of red lesions in digital fundus images. In: Proceedings of the IEEE international conference 2009, ISBI, Boston, pp 558–561

    Google Scholar 

  12. Jadhav A, Patil P (2015) Classification of diabetes retina images using blood vessel area. Int J Cybern Inform 4(2):251–257

    Google Scholar 

  13. Sopharak A, Uyyanonvara B, Barman S (2011) Automatic microaneurysm detection from non-dilated diabetic retinopathy retinal images using mathematical morphology methods. Int J Comput Sci 38(3):1–7

    Google Scholar 

  14. Shivaram J, Patil R, Aravind H (2009) Automated detection and quantification of haemorrhages in diabetic retinopathy images using image arithmetic and mathematical morphology methods. Int J Recent Trends Eng 2:174–176

    Google Scholar 

  15. Karnowski T, Govindasamy V, Tobin K, Chaum E, Abramoff M (2008) Retina lesion and microaneurysm segmentation using morphological reconstruction methods with ground-truth data. In: Proceedings of the IEEE international conference 2008, EMBS, Canada, pp 5433–5436

    Google Scholar 

  16. Kande G, Tirumala S, Subbaiah P (2010) Automatic detection of microaneurysms and hemorrhages in digital fundus images. J Digit Imaging 23(4):430–437

    Google Scholar 

  17. Matei D, Matei R (2008) Detection of diabetic symptoms in retina images using analog algorithms. Int J Med Health Sci 2(9):323–326

    Google Scholar 

  18. Langroudi M, Sadjedi H (2010) A new method for automatic detection and diagnosis of retinopathy diseases in colour fundus images based on morphology. In: Proceedings of the IEEE international conference on bioinformatics and biomedical technology 2010, China, pp 134–138

    Google Scholar 

  19. Acharya U, Lim C, Ng E, Chee C, Tamura T (2009) Computer-based detection of diabetes retinopathy stages using digital fundus images. J Eng Med 223(5):545–553

    Google Scholar 

  20. Singh N, Tripathi R (2010) Automated early detection of diabetic retinopathy using image analysis techniques. Int J Comput Appl 8(85):18–23

    Google Scholar 

  21. Niemeijer M, Ginneken V, Staal J, Suttorp-SchuIten A, Abrmoff M (2005) Automatic detection of red lesions in digital color fundus photograph. IEEE Trans Med Imaging 24(5):584–592

    Google Scholar 

  22. Sinthanayothin C, Boyce J, Williamson T, Cook H, Mensah E, Lal S (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19(2):105–112

    Google Scholar 

  23. Bae J, Kim K, Kang H, Jeong C, Park K, Hwang J (2011) A study on hemorrhage detection using hybrid method in fundus images. J Digit Imaging 24(3):394–404

    Google Scholar 

  24. Gardner G, Keating D, Williamson T (1996) Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol 80(11):940–944

    Google Scholar 

  25. Usher D, Dumskyj M, Himaga M, Williamson T, Nussey S, Boyce J (2004) Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med 21(1):84–90

    Google Scholar 

  26. Grinsven M, Venhuizen F, Ginneken B, Hoyng C, Theelen T, Sanchez C (2016) Automatic detection of hemorrhages on color fundus images using deep learning. Investig Ophthalmol Vis Sci 57(12):5966–5972

    Google Scholar 

  27. Khojasteh P, Aliahmad B, Kumar D (2018) Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms. BMC Ophthalmol 18(1):1–13

    Google Scholar 

  28. Eftekhari N, Pourreza H, Masoudi M, Shirazi K, Saeedi E (2019) Microaneurysm detection in fundus images using a two step convolutional neural network. BioMed Eng OnLine 18(67):1–10

    Google Scholar 

  29. Wang B, Xiao L, Liu Y, Wang J, Liu B, Li T, Ma X, Zhao Y (2018) Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage. Biosci Rep 38(6):1–24

    Google Scholar 

  30. Hatanaka Y, Nakagawa T, Hayashi Y, Hara T, Fujita H (2008) Improvement of automated detection method of hemorrhages in fundus images. In: Proceedings of the IEEE international conference on EMBS2008, Canada, pp 5429–5432

    Google Scholar 

  31. Pradhan S, Balasubramanian S, Chandrasekaran V (2008) An integrated approach using automatic seed generation and hybrid classification for the detection of red lesions in digital fundus images. In: Proceedings of the IEEE international conference on computer and information technology workshops, Sydney, pp 462–467

    Google Scholar 

  32. Tang L, Niemeijer M, Abramoff M (2011) Splat feature classification: detection of the presence of large retinal hemorrhages. In: Proceedings of the IEEE international conference on biomedical imaging: from nano to macro, Chicago, pp 681–684

    Google Scholar 

  33. Zhang X, Chutatape O (2005) Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy. In: Proceedings of the IEEE international conference on computer vision and pattern recognition CVPR2005, San Diego, pp 1–7

    Google Scholar 

  34. Sreeja K, Kumar S, Pradeep A (2020) Automated detection of retinal hemorrhage based on supervised classifiers and implementation in hardware. In: Proceedings of smart innovation, systems and technologies SIST 2020, Singapore, vol 182, pp 57–67

    Google Scholar 

  35. Kurale N, Vaidya M (2017) Retinal hemorrhage detection using splat segmentation of retinal fundus images. In: Proceedings of the IEEE international conference on conference on computing, communication, control and automation ICCUBEA 2017, Pune, pp 1–6

    Google Scholar 

  36. Kumar S, Nitta G (2019) Early detection of diabetic retinopathy in fundus images using GLCM and SVM. Int J Recent Technol Eng 7(5S4):17–20

    Google Scholar 

  37. Godlin L, Kumar P (2018) Detection of retinal hemorrhage from fundus images using ANFIS classifier and MRG segmentation. Biomed Res 29(7):1–9

    Google Scholar 

  38. Kose C, Sevik U, Ikiba C, Erdol H (2012) Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Comput Methods Programs Biomed 107(2):274–294

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank The Ophthalmology Department of Sri Aurobindo Seva Kendra, Kolkata, India for their clinical support.

Funding

This research activity is financially supported by the R&D Project, sponsored by the Department of Science and Technology and Biotechnology, Government of West Bengal, India (Memo No: 148(Sanc.)/ST/P/S&T/6G-13/2018).

Author information

Authors and Affiliations

  1. Department of Information Technology, Future Institute of Engineering and Management, Kolkata, West Bengal, India

    Niladri Sekhar Datta

  2. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Koushik Majumder

  3. Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India

    Amritayan Chatterjee & Himadri Sekhar Dutta

  4. Department of Ophthalmology, Sri Aurobindo Seva Kendra, Kolkata, West Bengal, India

    Sumana Chatterjee

Authors
  1. Niladri Sekhar Datta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koushik Majumder
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amritayan Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Himadri Sekhar Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sumana Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sharda University, Greater Noida, Uttar Pradesh, India

    Ankur Choudhary

  2. Department of Computer Science and Engineering, Sharda University, Greater Noida, Uttar Pradesh, India

    Arun Prakash Agrawal

  3. Asia Pacific Centre for Analytics (APCA), Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia

    Rajasvaran Logeswaran

  4. Information Technology, University of South Florida Sarasota–Manatee Campus, Sarasota, FL, USA

    Bhuvan Unhelkar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Datta, N.S., Majumder, K., Chatterjee, A., Dutta, H.S., Chatterjee, S. (2021). A Review: Hemorrhage Detection Methodologies on the Retinal Fundus Image. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 778. Springer, Singapore. https://doi.org/10.1007/978-981-16-3067-5_27

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-3067-5_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3066-8

  • Online ISBN: 978-981-16-3067-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation