Retinal Fundus Image Enhancement Using Piecewise Gamma Corrected Dominant Orientation Based Histogram Equalization

Reddy, P. Sujith; Singh, Himanshu; Kumar, Arun; Balyan, L. K.; Lee, Heung-No

doi:10.1109/iccsp.2018.8524518

Cited by 31 publications

(6 citation statements)

References 17 publications

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“…Zhou et al [42] and Palanisamy et al [43] revealed that luminance and contrast were improved with γ-correction and contrast-limited adaptive histogram equalization. Reddy et al [44] used texture histogram equalization. Foracchia et al [45] and Leahy et al [46] proposed methods based on the estimation of degradation features, such as luminance, contrast, or illumination to achieve enhancement.…”

Section: Enhancement Of Retinal Fundus Imagesmentioning

confidence: 99%

A deep learning-based framework for retinal fundus image enhancement

et al. 2023

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Problem Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis. Aim This study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation. Method We propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-quality (HQ) and low-quality (LQ) fundus images from the Kangbuk Samsung Hospital’s health screening program and ophthalmology department from 2017 to 2019. Then, we used these dataset to develop data augmentation methods to simulate major aspects of retinal image degradation and to propose a customized convolutional neural network (CNN) architecture to enhance LQ images, depending on the nature of the degradation. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), r-value (linear index of fuzziness), and proportion of ungradable fundus photographs before and after the enhancement process are calculated to assess the performance of proposed model. A comparative evaluation is conducted on an external database and four different open-source databases. Results The results of the evaluation on the external test dataset showed an significant increase in PSNR and SSIM compared with the original LQ images. Moreover, PSNR and SSIM increased by over 4 dB and 0.04, respectively compared with the previous state-of-the-art methods (P < 0.05). The proportion of ungradable fundus photographs decreased from 42.6% to 26.4% (P = 0.012). Conclusion Our enhancement process improves LQ fundus images that suffer from complex degradation significantly. Moreover our customized CNN achieved improved performance over the existing state-of-the-art methods. Overall, our framework can have a clinical impact on reducing re-examinations and improving the accuracy of diagnosis.

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Section: Enhancement Of Retinal Fundus Imagesmentioning

confidence: 99%

A deep learning-based framework for retinal fundus image enhancement

et al. 2023

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“…However, such methods result in poor enhancement that can be attributed to both loss of information and over-enhancement of specific gray levels. Such methods are not adaptive and thus are inappropriate to provide contrast enhancement for the medical imaging domain [Reddy et al, 2018, Singh et al, 2016. Spectral methods use wavelet transforms for quality enhancement.…”

Section: Related Workmentioning

confidence: 99%

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Aleem¹,

Kumar²,

Little³

et al. 2022

24th Irish Machine Vision and Image Processing Conference

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Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learning algorithms is highly dependent on data quality. Particularly for the medical domain, it is an important aspect as medical data is very sensitive to quality and poor quality can lead to misdiagnosis. To improve the diagnostic performance, research has been done both in complex DL architectures and in improving data quality using dataset dependent static hyperparameters. However, the performance is still constrained due to data quality and overfitting of hyperparameters to a specific dataset. To overcome these issues, this paper proposes random data augmentation based enhancement. The main objective is to develop a generalized, data-independent and computationally efficient enhancement approach to improve medical data quality for DL. The quality is enhanced by improving the brightness and contrast of images. In contrast to the existing methods, our method generates enhancement hyperparameters randomly within a defined range, which makes it robust and prevents overfitting to a specific dataset. To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks. For grayscale imagery, experiments have been performed with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets. Experimental results demonstrate that with the proposed enhancement methodology, DL architectures outperform other existing methods. Our code is publicly available at: https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement.

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“…The edge is smoothed by using a filtering mechanism. Reddy et al [21] presented an integrated enhancement algorithm to perform optimal enhancement of retinal images. The authors have used a flight firefly optimization approach with adaptive gamma-corrected and texture HE framework for improving overall visual quality.…”

Section: Related Workmentioning

confidence: 99%

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

Krishnegowda¹,

Ganesan²

2022

IJECE

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<p>Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification.</p>

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Retinal Fundus Image Enhancement Using Piecewise Gamma Corrected Dominant Orientation Based Histogram Equalization

Cited by 31 publications

References 17 publications

A deep learning-based framework for retinal fundus image enhancement

A deep learning-based framework for retinal fundus image enhancement

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

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