A retinal image enhancement technique for blood vessel segmentation algorithm

Bandara, A. M. R. R.; Giragama, P. W. G. R. M. P. B.

doi:10.1109/iciinfs.2017.8300426

Cited by 57 publications

(23 citation statements)

References 14 publications

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“…Dai et al proposed a two-stage denoising method including fourth-order partial differential equations (PDEs) and a relaxed median filter for retinal image enhancement [27]. Bandara and Giragama applied a spatially adaptive contrast-enhancement technique for enhancement of fundus images [28]. However, some retinal pathologies (e.g., hemorrhages, microaneurysms, and drusen) are mostly only a The Δ MOS was calculated as the difference between the original and enhanced ODP scores.…”

Section: Discussionmentioning

confidence: 99%

Deep-learning-based enhanced optic-disc photography

Sun

Kim

et al. 2020

PLoS ONE

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Optic-disc photography (ODP) has proven to be very useful for optic nerve evaluation in glaucoma. In real clinical practice, however, limited patient cooperation, small pupils, or media opacities can limit the performance of ODP. The purpose of this study was to propose a deep-learning approach for increased resolution and improved legibility of ODP by contrast, color, and brightness compensation. Each high-resolution original ODP was transformed into two counterparts: (1) down-scaled 'low-resolution ODPs', and (2) 'compensated high-resolution ODPs' produced via enhancement of the visibility of the optic disc margin and surrounding retinal vessels using a customized image post-processing algorithm. Then, the differences between these two counterparts were directly learned through a super-resolution generative adversarial network (SR-GAN). Finally, by inputting the high-resolution ODPs into SR-GAN, 4-times-up-scaled and overall-color-and-brightness-transformed 'enhanced ODPs' could be obtained. General ophthalmologists were instructed (1) to assess each ODP's image quality, and (2) to note any abnormal findings, at 1-month intervals. The image quality score for the enhanced ODPs was significantly higher than that for the original ODP, and the overall optic disc hemorrhage (DH)-detection accuracy was significantly higher with the enhanced ODPs. We expect that this novel deep-learning approach will be applied to various types of ophthalmic images.

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Section: Discussionmentioning

confidence: 99%

Deep-learning-based enhanced optic-disc photography

Sun

Kim

et al. 2020

PLoS ONE

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“…This section presents an assessment of the suitability of a recently invented spatially adaptive contrast enhancement technique for enhancing retinal fundus images for blood vessel segmentation. The enhancement technique was integrated with a variant of Tyler Coye algorithm, which has been improved with Hough line transformation based vessel reconstruction method [1]. Figure 2 shows the flow chart of the Tyler Coye algorithm for blood vessel segmentation in retinal images.…”

Section: Retinal Blood Vessel Segmentation Using Global Threshold Methodsmentioning

confidence: 99%

Blood Vessels Segmentation From Retinal Images for Diabetic Retinopathy Detection.

LaxmanJadhav¹

2019

IJAR

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“…In the image processing, image quality enhancement is also an important task that can help the researchers to get more accurate detection and classification results. Bandara and Giragama [58] introduced an image enhancement technique to make segments of blood vessels through fundus photographs. The image enhancement approach was combined with the Tyler Coye algorithmic approach.…”

Section: ) Blood Vessel Segmentation Techniquesmentioning

confidence: 99%

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

et al. 2020

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Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a timeconsuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community. INDEX TERMS Artificial intelligence, deep learning, diabetic retinopathy, fundus images, machine learning, ophthalmology.

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A retinal image enhancement technique for blood vessel segmentation algorithm

Cited by 57 publications

References 14 publications

Deep-learning-based enhanced optic-disc photography

Deep-learning-based enhanced optic-disc photography

Blood Vessels Segmentation From Retinal Images for Diabetic Retinopathy Detection.

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

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