Decorrelation stretch for enhancing colour fundus photographs affected by cataracts

Abstract

ABSTRACTA method of enhancing colour fundus photographs is proposed to reduce the effect of cataracts. The enhancement method employs a decorrelation stretch (DS) technique in an LCC colour model. The initial designed technique embeds Hubbard’s colouration model into DS parameters to produce enhanced results in a standard form of age-related macular degeneration (AMD) reading centres. The colouration model could modify to enhance the colour of lesions observed in diabetic retinopathy (DR). The proposed algorithm could improve the effect of cataracts on fundus images and provided good results when the density of the cataract was less than grade 2. In the case of images taken through cataracts higher than or equal to grade 2, some output results could become unusable when the cataract was in line with the macula.KEYWORDS: Decorrelation stretchretinal image enhancementcataract Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research has received funding support from the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation [grant number B04G640070].Notes on contributorsPreecha VonghirandechaPreecha Vonghirandecha is an assistant professor at the Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand. His current research interests include data Science, image processing and artificial intelligence applied to medical image analysis. He received a PhD in computer engineering from Prince of Songkla University, Thailand, in 2019.Supaporn KansomkeatSupaporn Kansomkeat is an assistant professor at the Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand. Her current research interests include software testing, test process improvement and artificial intelligence applied to medical image analysis. She received a PhD in computer engineering from Chulalongkorn University, Thailand, in 2007.Patama BhurayanontachaiPatama Bhurayanontachai (MD.) is an Associate Professor at the Department of Ophthalmology, Prince of Songkla University, Songkhla, Thailand. She received a certificate in Clinical Fellowship in vitreoretinal surgery from Flinders Medical Centre, Australia, in 2005. Her current research interests involve medical retina, surgical retina, and artificial intelligence applied to clinical diagnosis.Pannipa Sae-UengPannipa Sae-Ueng is a lecturer at the Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand. She received her Ph.D. in Computer Science in 2022 at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia. Recently she has focused on research topics in data science and artificial intelligence.Sathit IntajagSathit Intajag received the M. Eng. and D. Eng. Degree in electrical engineering from the King Mongkut’s Institute of Technology Ladkrabang (KMITL), Thailand, in 1998 and 2005, respectively. He is an associate professor at the Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand. His research interests include signal processing, statistical analysis, and artificial intelligence.