Colour Image Enhancement Model of Retinal Fundus Image for Diabetic Retinopathy Recognition

Abstract

Diabetic retinopathy (DR) features are typically identified through ophthalmologist eye examinations, but these images often face
challenges like low contrast, non-uniform illumination, and colour inconsistency, affecting the diagnosis accuracy. Therefore, this study
introduces two novel techniques to improve image quality. One is applying colour image processing techniques to original retinal
fundus images, overcoming existing algorithm limitations. Firstly, a new colour correction algorithm was proposed based on Tuned
Brightness Controlled Single-Scale Retinex (TBCSSR) named Fuzzy TBCSSR Histogram Matching (fTBCSSRhm) to address the issue of colour inconsistency in the dataset. Secondly, based on hybrid particle swarm optimisation-contrast stretch (HPSOCS), the hybrid
of TBCSSR and HPSOCS named eTBCSSR-HPSOCS algorithm is introduced to tackle the limitations of the standard Particle Swarm
Optimisation (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. This technique combines
the L-component of the LAB colour model with an enhanced velocity mechanism in PSO and contrast stretching (lavHPSOCS). Its goal
is to fine-tune parameters automatically, reduce over-enhancement, avoid unwanted artefacts, and preserve intricate details. This approach improves optimisation by balancing exploration and exploitation and refining velocity control. The proposed algorithm underwent both qualitative and quantitative evaluations. Tests on 100 retinal fundus images from primary datasets were performed to benchmark the algorithm against three existing approaches. The results show that the qualitative performance of the proposed enhancement is more favourable to ophthalmologist specialists than other images. Quantitatively, eTBCSSR-HPSOCS outperforms others with the lowest mean squared error (MSE) of 42.72859, the highest peak signal-to-noise ratio (PSNR) of 32.768, and entropy of 0.977.