Comparative analysis of fundus image enhancement in detection of diabetic retinopathy

Yadav, Sharad Kumar; Kumar, Shailesh; Kumar, Basant; Gupta, Rajiv

doi:10.1109/r10-htc.2016.7906814

Cited by 23 publications

(10 citation statements)

References 10 publications

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“…Figure 8a-f shows the histogram variance performance of all applied arts with reference to an original image histogram. Since the good histogram is one, it has a flat and wide range of gray information [12]. As can be noticed in Figure 8a-f, the visual appearance and interpretation of the proposed art histogram was comprehensively better than HE, AHE, and CLAHE.…”

Section: Experimental Results and Settingsmentioning

confidence: 90%

“…These adaptive approaches applied the histogram on pixel levels to boost the regional contrast of the image. The AHE produces good results, but it has a slow processing speed and great influence on the amplification of noise [12]. To address these problems, a contrast-limited adaptive histogram equalization (CLAHE) procedure was released in Reference [13], which has been observed to be remarkable in the removal of high rations of noise, artifacts, and over-enhancement of bright regions from the images.…”

Section: Introductionmentioning

confidence: 99%

“…This seems to be computationally expensive, and obtained CLAHE results become worse than an original image [16]. Later, an exposure-based sub-image histogram equalization (ESIHE) was created to come up with a solution to regulate the enhancement rate, maintain brightness, and lessen noise level [12]. Yang et al [17] considered a bin underflow-bin overflow histogram equalization (BUBOHE) scheme to solve the HE problems.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

Qureshi

Shaheed

2019

Algorithms

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Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.

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Section: Experimental Results and Settingsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

Qureshi

Shaheed

2019

Algorithms

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“…However, contrast limited adaptive histogram equalization (CLAHE) is introduced as an improvised version of HE that uses a clip limiting mechanism, which reduces the over brightness and provides better enhancement results [14]. Yadav et al [15] mentioned HE is subjected to amplification of noise and over enhancement factor. Shamsudeen et al [16] introduced an improved HE mechanism to adjust brightness and noise elimination.…”

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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“…Fundus image contrast improvements has become an essential factor in getting more quantitative measurements from the images by computational algorithms. Various research have been published to enhance fundus images such as Histogram equalization (HE) [8], adaptive histogram equalization (AHE) [9], contrast-limited adaptive histogram equalization (CLAHE) [10], sub-image histogram equalization (ESIHE) [9], bin underflow-bin overflow histogram equalization (BUBOHE) [11], RGB image to Gray image [12], which are solved a different issues of image quality.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model

et al. 2022

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Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and care with DR will significantly minimize the chance of vision loss. In modern ophthalmology, retinal image analysis has become a popular approach to disease diagnosis. The ophthalmologists and computerized systems extensively employ fundus angiography to detect DRbased clinical signs for early detection of DR. fundus photographs are commonly prone to low contrast, noise, and irregular illumination issues due to the complexity of imaging environments such as imaging variety of angles and light conditions. This research presents an Algorithm for improving the quality of images to strengthen the standard of color fundus images by reducing the noise and improving the contrast. The approach includes two main stages: cropping the images to remove insignificant content, then applying the shape crop and gaussian blurring for noise reduction and contrast improvement. The experimental results are evaluated using two standard datasets EyePACS and MESSIDOR. It's clearly shown that the outcomes of feature extraction and classification of enhanced images is outperform the results without applying the enhancement approach. The improved algorithm is also tested in smart hospitals as an IoMT application.INDEX TERMS image enhancement; deep learning; diabetic retinopathy; retina; fundus image; smart healthcare.

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Comparative analysis of fundus image enhancement in detection of diabetic retinopathy

Cited by 23 publications

References 10 publications

A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

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

Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model

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