Retinal image quality assessment using generic image quality indicators

Dias, João M.S.; Oliveira, Carlos Manta; Cruz, Luís A. da Silva

doi:10.1016/j.inffus.2012.08.001

Cited by 79 publications

(39 citation statements)

References 49 publications

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“…Image cropping has the advantage of decreasing the images' size and in turn the processing time by removing the irrelevant nonretinal regions within the image. 16 The cropping algorithm employed is an adaptation of the method illustrated in Ref. 58.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, both these approaches depended on templates created from a small set of excellent retinal images, which do not sufficiently consider the natural variance in retinal images. Recently, generic approaches have evolved to use a combination of sharpness, statistical, and textural features [15][16][17] to evaluate image sharpness and illumination.…”

Section: Spatial Retinal Image Quality Assessmentmentioning

confidence: 99%

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Retinal image quality assessment based on image clarity and content

et al. 2016

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Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.

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Section: Resultsmentioning

confidence: 99%

Section: Spatial Retinal Image Quality Assessmentmentioning

confidence: 99%

Retinal image quality assessment based on image clarity and content

et al. 2016

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“…includes an image quality assessment algorithm which has been validated on publicly available data sets, 37 and a co-registration algorithm, which allows comparisons of the same location in the retina to be made between visits. 38 One study showed that Retmarker can detect referable retinopathy with a sensitivity of 95.8% and a specificity of 63.2%.…”

Section: Idx-drmentioning

confidence: 99%

An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness

Tufail

Kapetanakis

Salas-Vega

et al. 2016

Health Technol Assess

105

128

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Background Diabetic retinopathy screening in England involves labour-intensive manual grading of retinal images. Automated retinal image analysis systems (ARIASs) may offer an alternative to manual grading. Objectives To determine the screening performance and cost-effectiveness of ARIASs to replace level 1 human graders or pre-screen with ARIASs in the NHS diabetic eye screening programme (DESP). To examine technical issues associated with implementation. Design Observational retrospective measurement comparison study with a real-time evaluation of technical issues and a decision-analytic model to evaluate cost-effectiveness. Setting A NHS DESP. Participants Consecutive diabetic patients who attended a routine annual NHS DESP visit. Interventions Retinal images were manually graded and processed by three ARIASs: iGradingM (version 1.1; originally Medalytix Group Ltd, Manchester, UK, but purchased by Digital Healthcare, Cambridge, UK, at the initiation of the study, purchased in turn by EMIS Health, Leeds, UK, after conclusion of the study), Retmarker (version 0.8.2, Retmarker Ltd, Coimbra, Portugal) and EyeArt (Eyenuk Inc., Woodland Hills, CA, USA). The final manual grade was used as the reference standard. Arbitration on a subset of discrepancies between manual grading and the use of an ARIAS by a reading centre masked to all grading was used to create a reference standard manual grade modified by arbitration. Main outcome measures Screening performance (sensitivity, specificity, false-positive rate and likelihood ratios) and diagnostic accuracy [95% confidence intervals (CIs)] of ARIASs. A secondary analysis explored the influence of camera type and patients’ ethnicity, age and sex on screening performance. Economic analysis estimated the cost per appropriate screening outcome identified. Results A total of 20,258 patients with 102,856 images were entered into the study. The sensitivity point estimates of the ARIASs were as follows: EyeArt 94.7% (95% CI 94.2% to 95.2%) for any retinopathy, 93.8% (95% CI 92.9% to 94.6%) for referable retinopathy and 99.6% (95% CI 97.0% to 99.9%) for proliferative retinopathy; and Retmarker 73.0% (95% CI 72.0% to 74.0%) for any retinopathy, 85.0% (95% CI 83.6% to 86.2%) for referable retinopathy and 97.9% (95% CI 94.9 to 99.1%) for proliferative retinopathy. iGradingM classified all images as either ‘disease’ or ‘ungradable’, limiting further iGradingM analysis. The sensitivity and false-positive rates for EyeArt were not affected by ethnicity, sex or camera type but sensitivity declined marginally with increasing patient age. The screening performance of Retmarker appeared to vary with patient’s age, ethnicity and camera type. Both EyeArt and Retmarker were cost saving relative to manual grading either as a replacement for level 1 human grading or used prior to level 1 human grading, although the latter was less cost-effective. A threshold analysis testing the highest ARIAS cost per patient before which ARIASs became more expensive per appropriate outcome than human grading, when used to replace level 1 grader, was Retmarker £3.82 and EyeArt £2.71 per patient. Limitations The non-randomised study design limited the health economic analysis but the same retinal images were processed by all ARIASs in this measurement comparison study. Conclusions Retmarker and EyeArt achieved acceptable sensitivity for referable retinopathy and false-positive rates (compared with human graders as reference standard) and appear to be cost-effective alternatives to a purely manual grading approach. Future work is required to develop technical specifications to optimise deployment and address potential governance issues. Funding The National Institute for Health Research (NIHR) Health Technology Assessment programme, a Fight for Sight Grant (Hirsch grant award) and the Department of Health’s NIHR Biomedical Research Centre for Ophthalmology at Moorfields Eye Hospital and the University College London Institute of Ophthalmology.

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“…Automated image quality evaluation [23] enables, for example, the early identification of camera problems affecting image quality and, thus, timely repairs and service to the equipment. The traceability between patient images and camera operators also allows that graders and photographers can pinpoint and address issues such as correct field definition to ensure better images.…”

Section: Discussionmentioning

confidence: 99%

“…RetmarkerSR automated analysis also includes a component of image quality analysis [23]. Images with quality problems are sent for human grading.…”

Section: Methodsmentioning

confidence: 99%

Screening for Diabetic Retinopathy in the Central Region of Portugal. Added Value of Automated ‘Disease/No Disease' Grading

Ribeiro

Oliveira²,

Neves

et al. 2014

Ophthalmologica

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Purpose: To describe the procedures of a nonmydriatic diabetic retinopathy (DR) screening program in the Central Region of Portugal and the added value of the introduction of an automated disease/no disease analysis. Methods: The images from the DR screening program are analyzed in a central reading center using first an automated disease/no disease analysis followed by human grading of the disease cases. The grading scale used is as follows: R0 - no retinopathy, RL - nonproliferative DR, M - maculopathy, RP - proliferative DR and NC - not classifiable. Results: Since the introduction of automated analysis in July 2011, a total of 89,626 eyes (45,148 patients) were screened with the following distribution: R0 - 71.5%, RL - 22.7%, M - 2.2%, RP - 0.1% and NC - 3.5%. The implemented automated system showed the potential for human grading burden reduction of 48.42%. Conclusions: Screening for DR using automated analysis allied to a simplified grading scale identifies DR vision-threatening complications well while decreasing human burden.

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Retinal image quality assessment using generic image quality indicators

Cited by 79 publications

References 49 publications

Retinal image quality assessment based on image clarity and content

Retinal image quality assessment based on image clarity and content

An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness

Screening for Diabetic Retinopathy in the Central Region of Portugal. Added Value of Automated ‘Disease/No Disease' Grading

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