Visual MRI : Merging information visualization and non-parametric clustering techniques for MRI dataset analysis
Artificial Intelligence in Medicine, ISSN: 0933-3657, Vol: 44, Issue: 3, Page: 183-199
2008
- 8Citations
- 47Captures
Metric Options: Counts1 Year3 YearSelecting 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.
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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.
Metrics Details
- Citations8
- Citation Indexes8
- CrossRef6
- Captures47
- Readers47
- 47
Article Description
This paper presents Visual MRI, an innovative tool for the magnetic resonance imaging (MRI) analysis of tumoral tissues. The main goal of the analysis is to separate each magnetic resonance image in meaningful clusters, highlighting zones which are more probably related with the cancer evolution. Such non-invasive analysis serves to address novel cancer treatments, resulting in a less destabilizing and more effective type of therapy than the chemotherapy-based ones. The advancements brought by Visual MRI are two: first, it is an integration of effective information visualization (IV) techniques into a clustering framework, which separates each MRI image in a set of informative clusters; the second improvement relies in the clustering framework itself, which is derived from a recently re-discovered non-parametric grouping strategy, i.e., the mean shift.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0933365708000869; http://dx.doi.org/10.1016/j.artmed.2008.06.006; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=55749085265&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/18775655; https://linkinghub.elsevier.com/retrieve/pii/S0933365708000869; https://dx.doi.org/10.1016/j.artmed.2008.06.006
Elsevier BV
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