Document Type


Publication Date


Publication Title

SIAM Journal on Imaging Sciences


Geisel School of Medicine

Additional Department

Department of Mathematics


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.



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.