Document Type
Article
Publication Date
10-1-2009
Publication Title
SIAM Journal on Imaging Sciences
Department
Geisel School of Medicine
Additional Department
Department of Mathematics
Abstract
Postreconstructed and postregistered medical images are typically treated as the raw data, implicitly assuming that those operations are error free. We question this assumption and explore how the precision of reconstruction and affine registration can be assessed by the image covariance matrix and confidence interval, called the confidence eigenimage, using a statistical model-based approach. Various hypotheses may be tested after image reconstruction and registration using classical statistical hypothesis testing vehicles: Is there a statistically significant difference between images? Does the intensity at a specific location or area of interest belong to the “normal” range? Is there a tumor? Does the image require rigid registration? We illustrate statistical hypothesis testing with three examples: breast computed tomography, breast near infrared linear reconstruction, and brain magnetic resonance imaging.
DOI
10.1137/080722199
Original Citation
Demidenko E. Statistical Hypothesis Testing for Postreconstructed and Postregistered Medical Images. SIAM J Imaging Sci. 2009 Oct 1;2(4):1049-1067. doi: 10.1137/080722199. PMID: 20622937; PMCID: PMC2900857.
Dartmouth Digital Commons Citation
Demidenko, Eugene, "Statistical Hypothesis Testing for Postreconstructed and Postregistered Medical Images" (2009). Dartmouth Scholarship. 1496.
https://digitalcommons.dartmouth.edu/facoa/1496
