Yan, Q, Zhao, Y, Zheng, Y et al. (4 more authors) (2019) Automated Retinal Lesion Detection via Image Saliency Analysis. Medical Physics, 46 (10). pp. 4531-4544. ISSN 0094-2405
Abstract
Background and objective:The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion detection method by adapting the concepts of saliency. Methods :Retinal images are firstly segmented as superpixels, two new saliency feature representations: uniqueness and compactness, are then derived to represent the superpixels. The pixel level saliency is then estimated from these superpixel saliency values via a bilateral filter. These extracted saliency features form a matrix for low-rank analysis to achieve saliency detection. The precise contour of a lesion is finally extracted from the generated saliency map after removing confounding structures such as blood vessels, the optic disc, and the fovea. The main novelty of this method is that it is an effective tool for detecting different abnormalities at pixel-level from different modalities of retinal images, without the need to tune parameters. Results:To evaluate its effectiveness, we have applied our method to seven public datasets of diabetic and malarial retinopathy with four different types of lesions: exudate, hemorrhage, microaneurysms, and leakage. The evaluation was undertaken at pixel-level, lesion-level, or image-level according to ground truth availability in these datasets. Conclusions:The experimental results show that the proposed method outperforms existing state-of-the-art ones in applicability, effectiveness, and accuracy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 American Association of Physicists in Medicine. This is an author produced version of a paper published in Medical Physics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Feature; lesion detection; low rank; retinal image; saliency |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Oct 2019 10:50 |
Last Modified: | 05 Aug 2020 00:38 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/mp.13746 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151446 |