Enhancement Method for Color Retinal Fundus Images Based on Structural Details and Illumination Improvements

Bataineh, Bilal; Almotairi, Khaled H.

doi:10.1007/s13369-021-05429-6

Cited by 11 publications

(13 citation statements)

References 31 publications

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“…The workflow for the segmentation procedure is readily apparent in Figure 1. There are three primary sources of data for this research: DRIVE [21,29], IDRiD [34] and SUSTech-SYSU dataset. By previous researchers the DRIVE database was planned and developed to facilitate comparative studies on blood vessel segmentation in human retinal representations.…”

Section: ░ 3 Materials and Methodsmentioning

confidence: 99%

“…Technically, it is known that noise enters the retina fundus images in many cases, which needs to be removed. For this, researchers either normalize the images or use methods that would remove the non-uniform illumination [20,21]. Researchers have given empirical evidence that the green channel (RGB colour model) is the most appropriate for extracting red and yellow spots of the eyes [22].…”

Section: Role Of Image Processingmentioning

confidence: 99%

“…The Digital Retinal Images for Vessel Extraction (DRIVE) dataset [21] has been designed for the development and validation of retinal segmentation algorithms and not for DR detection and does not cover the full spectrum of DR severity, camera variations, and patient demographics. Hence, data fusion and data augmentation should be considered to overcome the inadequacies.…”

Section: General Segmentation Frameworkmentioning

confidence: 99%

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An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments

Datta

Das

Kumar

2021

IJEER

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The detection of eye illnesses requires a thorough inspection of all of the eye's structures. Most significantly, the presence of blood vessels, an optical disc, and any other unwelcome objects, if any are discovered, is critical in determining the type of eye disease present. Specifically, the goal of this research is to establish a thorough segmentation framework that will aid in the detection of anatomical anomalies in the eye. A novel segmentation technique for analyzing blood vessels, optical disc health, and the presence of exudates has been added into the software. - It was decided to add the detection of aberrant objects such as exudates into the algorithm in order to develop a generic segmentation method. In order to verify the accuracy of the segmentation at each stage, random sampling is employed at each stage. The segmentation is then validated using the intersection over union metric. The accuracy of the integrated segmentation method as a whole is 91.66 percent.

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Section: ░ 3 Materials and Methodsmentioning

confidence: 99%

Section: Role Of Image Processingmentioning

confidence: 99%

Section: General Segmentation Frameworkmentioning

confidence: 99%

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An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments

Datta

Das

Kumar

2021

IJEER

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“…These days, digital images are used widely because of the rapid development in capturing machines and computer vision technology. Usually, we deal with low-quality images with low contrast and poor illumination caused by various capturing conditions [1]- [3]. Low-quality visual images pose challenges to human perception, as well as image processing and computer vision applications [2], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Low-quality visual images pose challenges to human perception, as well as image processing and computer vision applications [2], [4], [5]. Therefore, the quality of images should be improved before they are used to make images highly appropriate to the human visual perception and be analyzed easily by machines and improve computer vision applications [1]- [3], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Brightness and Contrast Enhancement Method for Color Images Via Pairing Adaptive Gamma Correction and Histogram Equalization

Bataineh¹

2023

IJACSA

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For enhanced adaptability to poor light enhancement whilst achieving high image contrast, a new method for color image correction based on the advantages of non-linear function in grey transformation and histogram equalization techniques is proposed in this work. Firstly, the original red, green and blue (RGB) image is converted into the HSV color space, and the V channel is used for enhancement. An adaptive gamma generator is proposed to adaptively calculate gamma parameters in accordance with dark, medium, or bright image conditions. The computed gamma parameters are used to propose a cumulative distribution function that produces an optimized curve for illumination values. Next, a second modified equalization is performed to evenly correct the offset of the illumination curve values on the basis of the equal probability of the available values only. Finally, the processed V channel replaces the original V channel, and the new HSV model returns to the RGB color space. Experiments show that the proposed method can significantly improve the low contrast and poor illumination of the color image whilst preserving the color and details of the original image. Results from benchmark data sets and measurements indicate that the proposed method outperforms other state-of-the-art methods.

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Retinal Image Enhancement for Detection of Medical Complications—A Summary

Ashanand¹,

Kaur²

2022

Lecture Notes in Electrical Engineering

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Enhancement Method for Color Retinal Fundus Images Based on Structural Details and Illumination Improvements

Cited by 11 publications

References 31 publications

An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments

An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments

Brightness and Contrast Enhancement Method for Color Images Via Pairing Adaptive Gamma Correction and Histogram Equalization

Retinal Image Enhancement for Detection of Medical Complications—A Summary

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