Automated Detection and Segmentation of Exudates for the Screening of Background Retinopathy

Author:

Kaur Jaskirat1,Mittal Deepti2,Malebary Sharaf3ORCID,Nayak Soumya Ranjan4ORCID,Kumar Devendra5ORCID,Kumar Manoj67ORCID,Gagandeep 8,Singh Simrandeep9

Affiliation:

1. Department of Electronics and Communication Engineering, Punjab Engineering College (Deemed to be University), Sector 12, Chandigarh 160012, India

2. Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India

3. Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah 21911, Saudi Arabia

4. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India

5. Department of Computer Science, Wachemo University, Hosaena, Ethiopia

6. Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, UAE

7. MEU Research Unit, Middle East University, Amman 11831, Jordan

8. Computer Science Engineering Department, Chandigarh Engineering College, Mohali, India

9. Electronics and Communication Engineering Department, UCRD, Chandigarh University, Mohali, India

Abstract

Exudate, an asymptomatic yellow deposit on retina, is among the primary characteristics of background diabetic retinopathy. Background diabetic retinopathy is a retinopathy related to high blood sugar levels which slowly affects all the organs of the body. The early detection of exudates aids doctors in screening the patients suffering from background diabetic retinopathy. A computer-aided method proposed in the present work detects and then segments the exudates in the images of retina acquired using a digital fundus camera by (i) gradient method to trace the contour of exudates, (ii) marking the connected candidate pixels to remove false exudates pixels, and (iii) linking the edge pixels for the boundary extraction of exudates. The method is tested on 1307 retinal fundus images with varying characteristics. Six hundred and forty-nine images were acquired from hospital and the remaining 658 from open-source benchmark databases, namely, STARE, DRIVE MESSIDOR, DiaretDB1, and e-Ophtha. The exudates segmentation method proposed in this research work results in the retinal fundus image-based (i) accuracy of 98.04%, (ii) sensitivity of 95.345%, and (iii) specificity of 98.63%. The segmentation results for a number of exudates-based evaluations depict the average (i) accuracy of 95.68%, (ii) sensitivity of 93.44%, and (iii) specificity of 97.22%. The substantial combined performance at image and exudates-based evaluations proves the contribution of the proposed method in mass screening as well as treatment process of background diabetic retinopathy.

Publisher

Hindawi Limited

Subject

Health Informatics,Biomedical Engineering,Surgery,Biotechnology

Reference44 articles.

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