Implementation of image enhancement techniques for vein patterns in eye

Swamy, J. S.; Sakkara, Sumanth

doi:10.1109/scopes.2016.7955859

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…Various image enhancement techniques have evolved over the years, including contrast stretching (CS) (Al-amri et al, 2010), histogram equalisation (HE) (Gonzalez & Woods, 2008), image negative (IN) (Swamy & Sakkara, 2017), brightness enhancement (BE) (Mohammed et al, 2013), gray-level slicing (GL) (Dabass & Vig, 2018), and CLAHE (Bhatia & Kumar Rawat, 2018). CS is a popular technique for medical image enhancement due to its ability to improve contrast without altering histogram shape and enhance the visibility of details and features (Cao & Li, 2020;Kaur & Choudhary, 2012;Mohanapriya & Kalaavathi, 2014).…”

Section: Introductionmentioning

confidence: 99%

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

Mohd Sharif,

Harun,

Yusof

2024

JICT

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Diabetic retinopathy (DR) features are typically identified through ophthalmologist eye examinations, but these images often facechallenges like low contrast, non-uniform illumination, and colour inconsistency, affecting the diagnosis accuracy. Therefore, this studyintroduces two novel techniques to improve image quality. One is applying colour image processing techniques to original retinalfundus images, overcoming existing algorithm limitations. Firstly, a new colour correction algorithm was proposed based on TunedBrightness 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 hybridof TBCSSR and HPSOCS named eTBCSSR-HPSOCS algorithm is introduced to tackle the limitations of the standard Particle SwarmOptimisation (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. This technique combinesthe L-component of the LAB colour model with an enhanced velocity mechanism in PSO and contrast stretching (lavHPSOCS). Its goalis 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.

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