An Automatic Algorithm for Stitching Multi-Sequence Retinal Images
SSRN, ISSN: 1556-5068
2022
- 394Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Background and Objective: Registration of retinal color fundus images which were taken at different times, from different perspectives is a critical prerequisite for diagnosis of retinopathy or other eye diseases. For the sake of automatic stitching of multiple retinal images, which can improve the speed of image registration while ensuring the accuracy of matching, a novel automatic registration algorithm of stitching multi-sequence retinal image is proposed in the essay. Methods: The method based on SURT algorithm segments retinal images into small regions, where the area is independent of each other, and then extracts saliency features. The geometric relationship between the sequence retinal image and the reference sequence image is then estimated from the offset which was been calculated from the matched feature point pairs via MODE algorithm. Finally, we use the maximum value fusion to obtain precise fusion image seamlessly. The main innovation of this algorithm is that it is an available avenue for stitching retinal color fundus from multimodality automatically with high speed and accuracy, without the need to adjust parameters. Results: Some experiments demonstrate that the method is highly accurate image stitching, with significantly solved the errors caused by the limitation of the splicing sequence and the accumulation of splicing errors. And the results indicate that the novel algorithm outperforms on private and public medical image database.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85178529870&origin=inward; http://dx.doi.org/10.2139/ssrn.4188695; https://www.ssrn.com/abstract=4188695; https://dx.doi.org/10.2139/ssrn.4188695; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4188695; https://ssrn.com/abstract=4188695
Elsevier BV
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