Handling non-corresponding regions in image registration
Informatik aktuell, ISSN: 1431-472X, Page: 107-112
2015
- 3Citations
- 5Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
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.
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.
Conference Paper Description
Image registration is particularly challenging if the images to be aligned contain non-corresponding regions. Using state-of-the-art algorithms typically leads to unwanted and unrealistic deformations in these regions. There are various approaches handling this problem which improve registration results, however each with a focus on specific applications. In this note we describe a general approach which can be applied on different mono-modal registration problems. We show the effects of this approach compared to a standard registration algorithm on the basis of five 3D CT lung image pairs where synthetic tumors have been added. We show that our approach significantly reduces unwanted deformation of a non-corresponding tumor. The average volume decrease is 9% compared to 66% for the standard approach while the overall accuracy based on landmark error is retained.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85012216685&origin=inward; http://dx.doi.org/10.1007/978-3-662-46224-9_20; https://link.springer.com/10.1007/978-3-662-46224-9_20; https://dx.doi.org/10.1007/978-3-662-46224-9_20; https://link.springer.com/chapter/10.1007/978-3-662-46224-9_20
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know