@inproceedings{5c202e21c26f433b94d3da502b4fdf02,
title = "Nonparametric cluster analysis of autoradiographic images",
abstract = "This paper describes a novel non-parametric statistical method based on cluster analysis for localizing significant differences in autoradiographic image data sets under two conditions. By thresholding cluster-size rather than pixel-values to reject false positives, this approach enhances statistical power. This test makes no assumption as to probability distribution or other properties of the statistical parametric map (SPM). The computational burden entailed by Monte-Carlo method is also greatly reduced by a randomization method.",
author = "K. Yin and W. Zhao and Young, {T. Y.} and Ginsberg, {M. D.}",
year = "1999",
month = dec,
day = "1",
language = "English (US)",
isbn = "0780356756",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
publisher = "IEEE",
booktitle = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
note = "Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) ; Conference date: 13-10-1999 Through 16-10-1999",
}