Retinal image quality assessment for diabetic retinopathy screening: A survey

Jia-jun, Lin; Lun, Yu; Weng, Qian; Zheng, Xianghan

doi:10.1007/s11042-019-07751-6

Cited by 24 publications

(27 citation statements)

References 43 publications

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“…Khan et al [25] presented a review work on the fundus imaging techniques towards the precise extraction of blood vessels. Lin et al [26] carried a systematic analysis of retinal images for the automatic screening of the DR.…”

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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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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“…Precise artifact segmentation is useful as a preprocessing stage or as an image quality estimation index [30].…”

Section: B Artifact Segmentation and Inpaintingmentioning

confidence: 99%

Comparison of transverse mode instability in Yb, Tm and Ho doped bent high-power fiber amplifiers

Vazeerpour¹

2021

Opt. Pura Apl.

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Dust particle artifacts are present in all imaging modalities but have more adverse consequences in medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms. We propose a method for detecting and accurately segmenting dust artifacts in retinal images based on multi-scale template-matching on several input images and an iterative segmentation via an inpainting approach. The inpainting is done through dictionary learning and sparse-based representation. The artifact segmentation is refined by comparing the original image to the initial restoration. On average, 90% of the dust artifacts were detected in the test images, with state-of-theart restoration results. All detected artifacts were accurately segmented and removed. Even the most challenging artifacts located on top of blood vessels were removed. Thus, ensuring the continuity of the retinal structures. The proposed method successfully detects and removes dust artifacts in retinal images, which could be used to avoid false-positive lesion detections or as an image quality criterion. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsule.

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“…In the production of an offline, smartphone based, multi-class identifying DR diagnosis system for Ethiopia, the author recommends the development of a high quality portable fundus camera as the first priority over DL based fundus images classification. This is mainly because fundus images with reduced quality were found to be unsuitable for automated and manual diagnosis of DR (Yao et al, 2016;Lin et al, 2019). Producing high quality cameras would lead the way towards solving challenges associated with absence of medical equipments for diagnosing the disease (Foster & Resnikoff, 2005).…”

Section: Challenge Towards a Fully Offline Diabetic Retinopathy Diagn...mentioning

confidence: 99%

Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation

Hagos¹

2020

Preprint

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Introducing automated Diabetic Retinopathy (DR) diagnosis into Ethiopia is still a challenging task, despite recent reports that present trained Deep Learning (DL) based DR classifiers surpassing manual graders. This is mainly because of the expensive cost of conventional retinal imaging devices used in DL based classifiers. Current approaches that provide mobile based binary classification of DR, and the way towards a cheaper and offline multi-class classification of DR will be discussed in this paper.

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Retinal image quality assessment for diabetic retinopathy screening: A survey

Cited by 24 publications

References 43 publications

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

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

Comparison of transverse mode instability in Yb, Tm and Ho doped bent high-power fiber amplifiers

Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation

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