Comparison of Image Enhancement Methods for Diabetic Retinopathy Screening

Dafwen Toresa; Fana Wiza; Keumala Anggraini; Taslim Taslim; Edriyansyah; Lisnawita Lisnawita

doi:10.29207/resti.v7i5.5193

Dafwen Toresa Universitas Lancang Kuning
Fana Wiza Universitas Lancang Kuning
Keumala Anggraini Universitas Lancang Kuning
Taslim Taslim Universitas Lancang Kuning
Edriyansyah Universitas Hang Tuah Pekanbaru
Lisnawita Lisnawita Universitas Lancang Kuning

DOI: https://doi.org/10.29207/resti.v7i5.5193

Keywords: Diaebetic retinopathy, Fundus image, Image enhancement, Image Processing

Abstract

The most common factor contributing to visual abnormalities that result in blindness is known as diabetic retinopathy (DR). Retinal fundus scanning, a non-invasive method that is integral to the picture pre-processing phase, can be used to identify and monitor DR. Low intensity, irregular lighting, and inhomogeneous color are some of the main issues with DR fundus photographs. Analysis of aberrant characteristics on retinal fundus images to identify diabetic retinopathy is one of the key responsibilities of image enhancement. However, a variety of approaches have been created and it is unknown whether one is best suited for use with images of the retinal fundus. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. The contrast-limited adaptive histogram equalization (CLAHE) method, the gray-level slicing method, the median filtering method, and the low light method are image improvement methods used to enhance images of the retinal fundus. The parameters Natural Image Quality Evaluator (NIQE), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and entropy will be used to assess each image enhancement technique's performance. An ophthalmologist from Sains University Hospital (HUSM) provided the image data. The findings indicate that while each technique has its own benefits, the CLAHE technique, with a standard deviation MSE of 0.0004, is the best.

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