Standard
Fast blur detection and parametric deconvolution of retinal fundus images. /
Williams, Bryan M.; Al-Bander, Baidaa; Pratt, Harry et al.
Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings. ed. / M. Jorge Cardoso; Tal Arbel. Cham: Springer-Verlag, 2017. p. 194-201 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10554 ).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Williams, BM, Al-Bander, B, Pratt, H, Lawman, S, Zhao, Y, Zheng, Y & Shen, Y 2017,
Fast blur detection and parametric deconvolution of retinal fundus images. in MJ Cardoso & T Arbel (eds),
Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10554 , Springer-Verlag, Cham, pp. 194-201, International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada,
14/09/17.
https://doi.org/10.1007/978-3-319-67561-9_22
APA
Williams, B. M., Al-Bander, B., Pratt, H., Lawman, S., Zhao, Y., Zheng, Y., & Shen, Y. (2017).
Fast blur detection and parametric deconvolution of retinal fundus images. In M. J. Cardoso, & T. Arbel (Eds.),
Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings (pp. 194-201). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10554 ). Springer-Verlag.
https://doi.org/10.1007/978-3-319-67561-9_22
Vancouver
Williams BM, Al-Bander B, Pratt H, Lawman S, Zhao Y, Zheng Y et al.
Fast blur detection and parametric deconvolution of retinal fundus images. In Cardoso MJ, Arbel T, editors, Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings. Cham: Springer-Verlag. 2017. p. 194-201. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2017 Sept 9. doi: 10.1007/978-3-319-67561-9_22
Author
Williams, Bryan M. ; Al-Bander, Baidaa ; Pratt, Harry et al. /
Fast blur detection and parametric deconvolution of retinal fundus images. Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings. editor / M. Jorge Cardoso ; Tal Arbel. Cham : Springer-Verlag, 2017. pp. 194-201 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Bibtex
@inproceedings{d5f9a8309d6c47318a81c6be5d2959a4,
title = "Fast blur detection and parametric deconvolution of retinal fundus images",
abstract = "Blur is a significant problem in medical imaging which can hinder diagnosis and prevent further automated or manual processing. The problem of restoring an image from blur degradation remains a challenging task in image processing. Semi-blind deblurring is a useful technique which may be developed to restore the underlying sharp image given some assumed or known information about the cause of degradation. Existing models assume that the blur is of a particular type, such as Gaussian, and do not allow for the approximation of images corrupted by other blur types which are not easily incorporated into deblurring frameworks. We present an automated approach to image deconvolution which assumes that the cause of blur belongs to a set of common types. We develop a hierarchical approach with convolutional neural networks (CNNs) to distinguish between blur types, achieving an accuracy of 0.96 across a test set of 900 images, and to determine the blur strength, achieving accuracy of 0.77 across 1500 test images. Given this, we are able to reconstruct the underlying image to mean ISNR of 7.53.",
keywords = "Colour fundus, Convolutional neural networks, Deconvolution, Parametric, Retina",
author = "Williams, {Bryan M.} and Baidaa Al-Bander and Harry Pratt and Samuel Lawman and Yitian Zhao and Yalin Zheng and Yaochun Shen",
year = "2017",
month = oct,
day = "1",
doi = "10.1007/978-3-319-67561-9_22",
language = "English",
isbn = "9783319675602",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "194--201",
editor = "Cardoso, {M. Jorge} and Tal Arbel",
booktitle = "Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings",
note = "International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 ; Conference date: 14-09-2017 Through 14-09-2017",
}
RIS
TY - GEN
T1 - Fast blur detection and parametric deconvolution of retinal fundus images
AU - Williams, Bryan M.
AU - Al-Bander, Baidaa
AU - Pratt, Harry
AU - Lawman, Samuel
AU - Zhao, Yitian
AU - Zheng, Yalin
AU - Shen, Yaochun
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Blur is a significant problem in medical imaging which can hinder diagnosis and prevent further automated or manual processing. The problem of restoring an image from blur degradation remains a challenging task in image processing. Semi-blind deblurring is a useful technique which may be developed to restore the underlying sharp image given some assumed or known information about the cause of degradation. Existing models assume that the blur is of a particular type, such as Gaussian, and do not allow for the approximation of images corrupted by other blur types which are not easily incorporated into deblurring frameworks. We present an automated approach to image deconvolution which assumes that the cause of blur belongs to a set of common types. We develop a hierarchical approach with convolutional neural networks (CNNs) to distinguish between blur types, achieving an accuracy of 0.96 across a test set of 900 images, and to determine the blur strength, achieving accuracy of 0.77 across 1500 test images. Given this, we are able to reconstruct the underlying image to mean ISNR of 7.53.
AB - Blur is a significant problem in medical imaging which can hinder diagnosis and prevent further automated or manual processing. The problem of restoring an image from blur degradation remains a challenging task in image processing. Semi-blind deblurring is a useful technique which may be developed to restore the underlying sharp image given some assumed or known information about the cause of degradation. Existing models assume that the blur is of a particular type, such as Gaussian, and do not allow for the approximation of images corrupted by other blur types which are not easily incorporated into deblurring frameworks. We present an automated approach to image deconvolution which assumes that the cause of blur belongs to a set of common types. We develop a hierarchical approach with convolutional neural networks (CNNs) to distinguish between blur types, achieving an accuracy of 0.96 across a test set of 900 images, and to determine the blur strength, achieving accuracy of 0.77 across 1500 test images. Given this, we are able to reconstruct the underlying image to mean ISNR of 7.53.
KW - Colour fundus
KW - Convolutional neural networks
KW - Deconvolution
KW - Parametric
KW - Retina
U2 - 10.1007/978-3-319-67561-9_22
DO - 10.1007/978-3-319-67561-9_22
M3 - Conference contribution/Paper
AN - SCOPUS:85029793738
SN - 9783319675602
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 194
EP - 201
BT - Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings
A2 - Cardoso, M. Jorge
A2 - Arbel, Tal
PB - Springer-Verlag
CY - Cham
T2 - International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 14 September 2017 through 14 September 2017
ER -
