Multi-Frame Super-Resolution with Quality Self-Assessment for Retinal Fundus Videos

Köhler T, Brost A, Mogalle K, Zhang Q, Köhler C, Michelson G, Hornegger J, Tornow RP (2014)

Publication Type: Conference contribution, Original article

Publication year: 2014

Publisher: Springer

Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

City/Town: Heidelberg

Pages Range: 650-657

Conference Proceedings Title: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2014

Event location: Cambridge, MA

URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2014/Koehler14-MSW.pdf

DOI: 10.1007/978-3-319-10404-1_81

Abstract

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. © 2014 Springer International Publishing.

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How to cite

APA:

Köhler, T., Brost, A., Mogalle, K., Zhang, Q., Köhler, C., Michelson, G.,... Tornow, R.-P. (2014). Multi-Frame Super-Resolution with Quality Self-Assessment for Retinal Fundus Videos. In Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2014 (pp. 650-657). Cambridge, MA: Heidelberg: Springer.

MLA:

Köhler, Thomas, et al. "Multi-Frame Super-Resolution with Quality Self-Assessment for Retinal Fundus Videos." Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Cambridge, MA Heidelberg: Springer, 2014. 650-657.

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