symmetry S S Article Enhance Contrast and Balance Color of Retinal Image Jessada Dissopa, Supaporn Kansomkeat and Sathit Intajag *   Citation: Dissopa, J.; Kansomkeat, S.; Intajag, S. Enhance Contrast and Balance Color of Retinal Image. Symmetry 2021, 13, 2089. https:// doi.org/10.3390/sym13112089 Academic Editor: Nikos Mastorakis Received: 6 September 2021 Accepted: 20 October 2021 Published: 4 November 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, Thailand; 6110220125@email.psu.ac.th (J.D.); supaporn.k@psu.ac.th (S.K.) * Correspondence: sathit.i@psu.ac.th Abstract: This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists of a few simple steps. Firstly, local image contrast is refined with the CLAHE (Contrast Limited Adaptive Histogram Equalization) technique by operating CIE L*a*b* color space. Then, the contrast-enhanced image is stretched and rescaled by a histogram scaling equation to adjust the overall brightness offsets of the image and standardize it to Hubbard’s retinal image brightness range. The proposed method was assessed with retinal images from the DiaretDB0 and STARE datasets. The findings in the experimentation section indicate that the proposed method results in delightful color naturalness along with a standard color of retinal lesions. Keywords: color retinal image; color balance; contrast enhancement; Rayleigh CLAHE; age-related macular degeneration 1. Introduction The World Health Organization (WHO) reported 65 million patients of AMD around the world, and the numbers could increase to 300 million patients by 2040 [1]. Currently, AMD evaluation is based on clinical retinal color photography analysis, which relies on camera properties and the retinal photographer’s experience. These images could be unsatisfactory for the experts to diagnose because of their low quality, such as low contrast, under and overexposure, etc. [2]. Hence, prior to usage, these low-quality images need to be enhanced to ameliorate a superior appearance of the retinal anatomical details. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a technique to increase the low contrast of an image [3]. It was developed from Histogram Equalization (HE) and provided a full range enhancement [4]. The global enhancement sometimes increases some noise or artifacts along with contrast because it amplifies all levels of light intensity, causing images to be too bright. Adaptive Histogram Equalization (AHE) [5], which is a local enhancement, was introduced to fix this issue in HE by distributing the overall brightness of the image to enhance contrast while disclosing hidden details. However, this approach still significantly amplifies noise, especially when applied to images with high noise levels, such as in medical images. Therefore, CLAHE was developed to address the above-mentioned issues, where the CLAHE algorithm sharpens images and limits noise. In order to categorize breast tumors, a classification technique for mammographic images was proposed by combining the machine learning techniques Gaussian Radial Basis Kernel ELM (Extreme Learning Machine) and KPCA (Kernel Principal Component Analysis) [6]. In the preprocessing step, CLAHE was applied to improve the quality of low-contrast images enhancing the hidden information in the mammograms. CLAHE did not only increase the contrast of the images but also limited the noise in the mammograms. To assist ophthalmologists, computer-aided diagnosis based on the enhancement of degraded fundus photographs made use of the CLAHE technique to improve retina color image quality via CIE L*a*b* color model [7]. First, the input image was converted to CIE Symmetry 2021, 13, 2089. https://doi.org/10.3390/sym13112089 https://www.mdpi.com/journal/symmetry