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Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment
2013
2013 IEEE 10th International Symposium on Biomedical Imaging
This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of
doi:10.1109/isbi.2013.6556567
pmid:24443688
pmcid:PMC3892706
dblp:conf/isbi/SharmaFGEVGSG13
fatcat:f6dga4ex5fhrvbvupyqf3nzygy