Characterisation of VBM Algorithms for Processing of Medical MRI Images
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11927 LNAI, Page: 443-448
2019
- 3Captures
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.
Metrics Details
- Captures3
- Readers3
Conference Paper Description
In Voxel-Based Morphometry (VBM), spatial normalisation is a major process which transforms images into a standard space and is often referred to as co-registration. This project is a comparison and observation of differences in the performance, measured as the overlap between images, of two co-registration algorithms used in VBM on human brain Magnetic Resonance Imaging (MRI) data. Here we show differences between genders and algorithms on specific regions of the brain using grey matter segments and unsegmented MRI images. Results show that there are significant differences in the overlap of regions depending on the algorithm which may be considered in addition to current knowledge on the subject. Importantly, we are interested in investigating what these differences mean to published and on-going research as well as observing whether said difference spans all the way to the Parahippocampal Gyrus and other important regions associated with psychological related diseases.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85076979933&origin=inward; http://dx.doi.org/10.1007/978-3-030-34885-4_34; http://link.springer.com/10.1007/978-3-030-34885-4_34; http://link.springer.com/content/pdf/10.1007/978-3-030-34885-4_34; https://dx.doi.org/10.1007/978-3-030-34885-4_34; https://link.springer.com/chapter/10.1007/978-3-030-34885-4_34
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