Automated quality assessment of retinal fundus photos

Paulus, Jan; Meier, Jörg; Bock, Rüdiger; Hornegger, Joachim; Michelson, Georg

doi:10.1007/s11548-010-0479-7

Cited by 86 publications

(57 citation statements)

References 12 publications

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“…Comparing our results with [12,11,4] we come very close to the best performing methods of [4,11]. However, we have used a much larger dataset that has images from various machines and imaging centres, thus demonstrating our method's robustness.…”

Section: Classification Resultssupporting

confidence: 73%

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Image Quality Classification for DR Screening Using Convolutional Neural Networks

Tennakoon

Mahapatra

Roy

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. The quality of input images significantly affects the outcome of automated diabetic retinopathy screening systems. Current methods to identify image quality rely on hand-crafted geometric and structural features, that does not generalize well. We propose a new method for retinal image quality classification (IQC) that uses computational algorithms imitating the working of the human visual systems. The proposed method leverages on learned supervised information using convolutional neural networks (CNN), thus avoiding hand-engineered features. Our analysis shows that the learned features capture both geometric and structural information relevant for image quality classification. Experimental results conducted on a relatively large dataset demonstrates that the overall method can achieve high accuracy. We also show that effective features for IQC can be learned by full training of shallow CNN as well as by using transfer learning.

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Section: Classification Resultssupporting

confidence: 73%

“…They require anatomical landmark segmentation which is complex and error prone, especially for poor quality images. The method proposed by Paulus et al [12] combined generic and structural image features but rely heavily on accurate landmark segmentation.…”

Section: Introductionmentioning

confidence: 99%

Image Quality Classification for DR Screening Using Convolutional Neural Networks

Tennakoon

Mahapatra

Roy

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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“…We obtain high sensitivity (correctly identified gradable images), specificity (correctly identified ungradable images) and accuracy values which outperforms current state-of-the-art methods for our dataset. These values are higher than those reported in the original works of [4,12] and significantly better than [14]. A significant achievement of our method is that it has been tested on a much larger dataset than previous works.…”

Section: Dataset Descriptionmentioning

confidence: 52%

“…To perform a comparative study, we have tested the methods proposed in [4], [14] and [12]. We re-implement these algorithms by closely following the details given in the respective works.…”

Section: Dataset Descriptionmentioning

confidence: 99%

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Retinal Image Quality Classification Using Neurobiological Models of the Human Visual System

Mahapatra

2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. Retinal image quality assessment (IQA) algorithms use different hand crafted features without considering the important role of the human visual system (HVS). We solve the IQA problem using the principles behind the working of the HVS. Unsupervised information from local saliency maps and supervised information from trained convolutional neural networks (CNNs) are combined to make a final decision on image quality. A novel algorithm is proposed that calculates saliency values for every image pixel at multiple scales to capture global and local image information. This extracts generalized image information in an unsupervised manner while CNNs provide a principled approach to feature learning without the need to define hand-crafted features. The individual classification decisions are fused by weighting them according to their confidence scores. Experimental results on real datasets demonstrate the superior performance of our proposed algorithm over competing methods.

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State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

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Automated quality assessment of retinal fundus photos

Cited by 86 publications

References 12 publications

Image Quality Classification for DR Screening Using Convolutional Neural Networks

Image Quality Classification for DR Screening Using Convolutional Neural Networks

Retinal Image Quality Classification Using Neurobiological Models of the Human Visual System

State of the Art in Vascular Biometrics

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