A Survey on Exudate Detection in Fundus Images

Authors

  • Apurva M Shet Department of Electronics and Communication, Sapthagiri College Of Engineering, Bengaluru
  • Deepika U M Department of Electronics and Communication, Sapthagiri College Of Engineering, Bengaluru
  • Chandana V Department of Electronics and Communication, Sapthagiri College Of Engineering, Bengaluru
  • G Meghashree Department of Electronics and Communication, Sapthagiri College Of Engineering, Bengaluru
  • Sudha M S Department of Electronics and Communication, Sapthagiri College Of Engineering, Bengaluru

Keywords:

Diabetic Retinopathy, Exudates, Segmentation, Machine learning

Abstract

Diabetic Retinopathy is often noticed in individuals suffering from diabetes since long time. Diabetic Retinopathy is a major disorder, which affects the patient’s eye, when left untreated could lead to permanent blindness and its major characteristics is presence of Exudates. Exudates are the fluid that leaks out of blood vessels. Detecting exudates help the
Ophthalmologist to diagnose the severity of the patient’s condition and in turn help in better medication. Detection of the exudates in the retina is done by various methods developed by researchers. This paper focuses on surveying few of the works published in this regard.

 

Downloads

Download data is not yet available.

References

Sudhir Rao Rupanangudi, Varsha G Bhat, Bharath kumar Revana,

Jeevitha Gowda Chandramouli, Sheetal Devegowda, Darshan G,

Kaushik Kumaran, Niyatha Malepati, Laveena Manjunath,

Shwetha N, Vaishnavi Ramsali “Optic Disk Extraction and Hard

Exudate identification in fundus images using computer vision

and Machine learning”, IEEE 11th Annual computing and

communication workshop, pp 655-661, 2021.

Yongshuo Zong, Jinling Chen, Lvquig Yang, siyi Tao,

Cieryouzhen Aoma, Jiangsheng Zhao, And Shui-Hua Wang,“Unet Based Method For Automatic Hard Exudates Segementation

in Fundus Images Using Inception Module And Residual

Connection”, IEEE 2020.

Dhanshree Thulkar, Rohin Daruwala “Detection of Exudates for

Diabetic Macular Edema classification”, 5th International

conference on science and technology (ICST), Yogyakarta,

Indonesia 2019.

Pradip Dhal, Chandrashekhar Azad “A Novel approach for blood

vessel segmentation with Exudate detection in diabetic

retinopathy”, International Conference on Artificial intelligence

and signal processing (AISP), 2020.

Aishwarya K Dixit, Dr. Parimala Prabhakar “Hard Exudates

detection using linear brightness method”, 2019 4th International

Conference On Recent Trends On Electronics, Information,

Communication & Technology (RTEICT-2019), MAY 17th &

th 2019.

Dulanji Lokuarachchi, Kasun Gunarathna, Lahiru Muthumal,

Tharindu Gamage, “Automated Detection of Exudates in Retinal

Images”, IEEE 15th International Colloquium on Signal

Processing and its applications (CSPA 2019), Penang, Malaysia,

-9 March 2019.

Vasanthi Satyananda, K V Narayanaswamy, Karibasappa “Hard

Exudate Extraction from fundus images using Watershed

Transform”, Indonesian Journal of Electrical Engineering and

Information (IJEEI).

P.R. Asha, S. Karpagavalli “Diabetic Retinal Exudates Detection

using Machine Learning techniques”, 2015 International

Conference on Advanced Computing and Communication

Systems (ICACCS-2015), Coimbatore, INDIA Jan ,05-07, 2015.

Mohammed Shafeeq Ahmed, Baddam Indira “Detection of

Exuadates from RGB Fundus Images Using 3? Control Method”,

IEEE WISPNET conference 2017.

R.Priya, P.Aruna “SVM and Neural Network based Diagnosis of

Diabetic Retinopathy”, International Journal of Computer

Applications(0975-8887)March2012.

Downloads

Published

2022-06-13

How to Cite

[1]
A. M. Shet, D. U M, C. V, G. Meghashree, and S. M S, “A Survey on Exudate Detection in Fundus Images”, pices, pp. 1-4, Jun. 2022.

Issue

2022: PiCES - Special Issue

Section

Articles

License

Copyright (c) 2022 Perspectives in Communication, Embedded-systems and Signal-processing - PiCES

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.