Intensity Index Based Histogram Equalization Technique for retinal Image Enhancement and Classification of Hard Exudates using Supervised Learning
Arun Pradeep1, X. Felix Joseph2

1Arun Pradeep, Department of Electronics and Communication Engineering, Noorul Islam University, Kanyakumari, Tamil Nadu, India.
2 X Felix Joseph, Department of Electrical and Electronics Engineering, NICHE & Bulehora University, Ethiopia.

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 1708-1714 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7732068519/19©BEIESP
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Abstract: An efficient, patient friendly method to detect retinal exudates based on binary operation is presented in this study. A novel Histogram equalization technique centered on intensity index is used for fundus image enhancement. Following the elimination of optic disc from the fundus image, morphological operation is performed to detect the exudate pixels. Finally, classification of hard exudates using a trained Support Vector Machine (SVM) classifier is implemented and evaluated using five different performance parameters. The results are assuring and recommends that the proposed method can be utilized as an analytic aid to ophthalmologist for early detection of retinopathy symptoms.
Keywords: Diabetic Retinopathy, Image Enhancement, Exudate detection, Support Vector Machine Classification.

Scope of the Article: Classification