An Automatic Quantitative Measurement Method for Performance Assessment of Retina Image Registration Algorithms

An Automatic Quantitative Measurement Method for Performance Assessment of Retina Image Registration Algorithms
Title:
An Automatic Quantitative Measurement Method for Performance Assessment of Retina Image Registration Algorithms
Other Titles:
2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
Keywords:
Publication Date:
16 August 2016
Citation:
E. P. Ong, J. A. Lee, G. Xu, B. H. Lee and D. W. K. Wong, "An automatic quantitative measurement method for performance assessment of retina image registration algorithms," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2016, pp. 3252-3255. doi: 10.1109/EMBC.2016.7591422
Abstract:
This paper presents a novel automatic quantitative measurement method for assessment of the performance of image registration algorithms designed for registering retina fundus images. To achieve automatic quantitative measurement, we propose the use of edges and edge dissimilarity measure for determining the performance of retina image registration algorithms. Our input is the registered pair of retina fundus images obtained using any of the existing retina image registration algorithms in the literature. To compute edge dissimilarity score, we propose an edge dissimilarity measure that we called “robustified Hausdorff distance”. We show that our proposed approach is feasible as designed by drawing comparison to visual evaluation results when tested on images from the DRIVERA and G9 dataset.
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PublisherCopyrights
Funding Info:
Description:
(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1558-4615
ISBN:
978-1-4577-0220-4
978-1-4577-0219-8
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