Retinal Exudates Detection Using Binary Operation and Hard Exudates Classification Using Support Vector Machine
Arun Pradeep1, X. Felix Joseph2
1NArun Pradeep, Department of Electronics and Communication Engineering, Noorul Islam University, Kanyakumari, Tamil Nadu, India.
2X Felix Joseph, Department of Electrical and Electronics Engineering, NICHE & Bulehora University, Ethiopia
Manuscript received on 29June 2019 | Revised Manuscript received on 05July 2019 | Manuscript published on 30 July 2019 | PP: 149-154 | Volume-8 Issue-9, July 2019 | Retrieval Number: I7572078919/19©BEIESP | DOI: 10.35940/ijitee.I7572.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Retinal exudates considered as a symptom of Diabetic retinopathy(DR) is one of most significant reason for visual deficiency. This paper focusses on early detection of hard exudates and to diagnose DR. Binary operations based exudate detection and SVM based hard exudate classification is discussed in this study. The RGB channel of fundus image is converted to HSI colour space for improved noise suppression and optic disc is eliminated preservinsg the blood vessels. In the final stage, hard exudates are classified using SVM classification. In order to evaluate the proposed approach, experiment tests are carried out on different set of images and the results are verified. The results are promising and suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening of DR.
Keywords: Retinal exudate detection, CLAHE, Morphological processing, Optic disc elimination, SVM classification.
Scope of the Article: Artificial Intelligence and Machine Learning