Multi-frame super-resolution with quality self-assessment for retinal fundus videos

This paper proposes a novel super-resolution framework to reconstruct high-resolution fundus images from multiple low-resolution video frames in retinal fundus imaging. Natural eye movements during an examination are used as a cue for super-resolution in a robust maximum a-posteriori scheme. In orde...

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Bibliographic Details
Published inMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 17; no. Pt 1; p. 650
Main Authors Köhler, Thomas, Brost, Alexander, Mogalle, Katja, Zhang, Qianyi, Köhler, Christiane, Michelson, Georg, Hornegger, Joachim, Tornow, Ralf P
Format Journal Article
LanguageEnglish
Published Germany 2014
Subjects
Algorithms
Feedback
Fundus Oculi
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Reproducibility of Results
Retinal Vessels - anatomy & histology
Retinoscopy - methods
Sensitivity and Specificity
Subtraction Technique
Video Recording - methods
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Summary:This paper proposes a novel super-resolution framework to reconstruct high-resolution fundus images from multiple low-resolution video frames in retinal fundus imaging. Natural eye movements during an examination are used as a cue for super-resolution in a robust maximum a-posteriori scheme. In order to compensate heterogeneous illumination on the fundus, we integrate retrospective illumination correction for photometric registration to the underlying imaging model. Our method utilizes quality self-assessment to provide objective quality scores for reconstructed images as well as to select regularization parameters automatically. In our evaluation on real data acquired from six human subjects with a low-cost video camera, the proposed method achieved considerable enhancements of low-resolution frames and improved noise and sharpness characteristics by 74%. In terms of image analysis, we demonstrate the importance of our method for the improvement of automatic blood vessel segmentation as an example application, where the sensitivity was increased by 13% using super-resolution reconstruction.