Diabetic retinopathy image database(DRiDB): A new database for diabetic retinopathy screening programs research

Prentašić, Pavle; Lončarić, Sven; Vatavuk, Zoran; Benčić, Goran; Subašić, Marko; Petković, Tomislav; Dujmović, Lana; Malenica-Ravlic, Maja; Budimlija, Nikolina; Tadić, Rašeljka

doi:10.1109/ispa.2013.6703830

Cited by 51 publications

(23 citation statements)

References 12 publications

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“…The publicly available DIARETDB1, 49 DRiDB, 50 and DMED 51 datasets were used for training and testing the proposed saliency computation stage. Preprocessing consisted of illumination correction, 52 fundus extension to remove the black mask region, 53 detection of vessels, 54 and optic disk using circular Hough transform.…”

Section: Methodsmentioning

confidence: 99%

“…Quantitative evaluation was performed on both abnormal and normal images (see Table 3). A set of 33 normal images, used for testing both S HE and s HM , was taken from DRiDB 50 and DIARETDB0. 57 A set of 12(13) abnormal images, used for testing S HE ðS HM Þ, was taken from DRiDB, 50 DMED, 51 and DIARETDB1 49 as mentioned in Sec.…”

Section: Evaluation Of Saliencymentioning

confidence: 99%

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Assistive lesion-emphasis system: an assistive system for fundus image readers

Rangrej

Sivaswamy

2017

J. Med. Imag

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Computer-assisted diagnostic (CAD) tools are of interest as they enable efficient decision-making in clinics and the screening of diseases. The traditional approach to CAD algorithm design focuses on the automated detection of abnormalities independent of the end-user, who can be an image reader or an expert. We propose a reader-centric system design wherein a reader's attention is drawn to abnormal regions in a least-obtrusive yet effective manner, using saliency-based emphasis of abnormalities and without altering the appearance of the background tissues. We present an assistive lesion-emphasis system (ALES) based on the above idea, for fundus image-based diabetic retinopathy diagnosis. Lesion-saliency is learnt using a convolutional neural network (CNN), inspired by the saliency model of Itti and Koch. The CNN is used to fine-tune standard low-level filters and learn high-level filters for deriving a lesion-saliency map, which is then used to perform lesion-emphasis via a spatially variant version of gamma correction. The proposed system has been evaluated on public datasets and benchmarked against other saliency models. It was found to outperform other saliency models by 6% to 30% and boost the contrast-to-noise ratio of lesions by more than 30%. Results of a perceptual study also underscore the effectiveness and, hence, the potential of ALES as an assistive tool for readers.

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Section: Methodsmentioning

confidence: 99%

Section: Evaluation Of Saliencymentioning

confidence: 99%

Assistive lesion-emphasis system: an assistive system for fundus image readers

Rangrej

Sivaswamy

2017

J. Med. Imag

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“…The ImageRet dataset consists of DIARETDB0 13 and DI-ARETDB1. 14 In addition to the above-mentioned datasets, there are other datasets, such as E-Ophtha, 20 ROC, 21 ARIA Online, 22 and DRiDB, 23 where interested readers can explore the details on their own.…”

Section: Imageretmentioning

confidence: 99%

A Survey on Intelligent Screening for Diabetic Retinopathy

Dai¹,

Zhu²,

Xi³

et al. 2019

Chinese Medical Sciences Journal

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Diabetic retinopathy (DR) is one of the leading causes of vision loss and can be effectively avoided by screening, early diagnosis and treatment. In order to increase the universality and efficiency of DR screening, many efforts have been invested in developing intelligent screening, and there have been great advances. In this paper, we survey DR screening from four perspectives: 1) public color fundus image datasets of DR; 2) DR classification and related lesion-extraction approaches; 3) existing computer-aided systems for DR screening; and 4) existing issues, challenges, and research trends. Our goal is to provide insights for future research directions on DR intelligent screening.

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“…There are several datasets for fundus retinal images that are publicly available for the researchers from all over the world. High-Resolution Fundus Image Database (HRFID) and Digital Retinal Images for Vessel Extraction (DRIVE) are two kinds of publicly open datasets that can be accessed by the researchers [ 6 , 27 ]. Researchers from digital image processing discipline can use images from these datasets as the raw materials for their experiment.…”

Section: Introductionmentioning

confidence: 99%

A Review on the Extraction of Quantitative Retinal Microvascular Image Feature

Kipli

Hoque

Lim

et al. 2018

Computational and Mathematical Methods in Medicine

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Digital image processing is one of the most widely used computer vision technologies in biomedical engineering. In the present modern ophthalmological practice, biomarkers analysis through digital fundus image processing analysis greatly contributes to vision science. This further facilitates developments in medical imaging, enabling this robust technology to attain extensive scopes in biomedical engineering platform. Various diagnostic techniques are used to analyze retinal microvasculature image to enable geometric features measurements such as vessel tortuosity, branching angles, branching coefficient, vessel diameter, and fractal dimension. These extracted markers or characterized fundus digital image features provide insights and relates quantitative retinal vascular topography abnormalities to various pathologies such as diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack, neovascular glaucoma, and cardiovascular diseases. Apart from that, this noninvasive research tool is automated, allowing it to be used in large-scale screening programs, and all are described in this present review paper. This paper will also review recent research on the image processing-based extraction techniques of the quantitative retinal microvascular feature. It mainly focuses on features associated with the early symptom of transient ischemic attack or sharp stroke.

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Diabetic retinopathy image database(DRiDB): A new database for diabetic retinopathy screening programs research

Cited by 51 publications

References 12 publications

Assistive lesion-emphasis system: an assistive system for fundus image readers

Assistive lesion-emphasis system: an assistive system for fundus image readers

A Survey on Intelligent Screening for Diabetic Retinopathy

A Review on the Extraction of Quantitative Retinal Microvascular Image Feature

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