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.
| Original language | English |
|---|---|
| Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | IEEE |
| Volume | 2 |
| ISBN (Print) | 0780356756 |
| State | Published - Dec 1 1999 |
| Event | 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) - Atlanta, GA, USA Duration: Oct 13 1999 → Oct 16 1999 |
Other
| Other | 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) |
|---|---|
| City | Atlanta, GA, USA |
| Period | 10/13/99 → 10/16/99 |
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ASJC Scopus subject areas
- Bioengineering
Cite this
Nonparametric cluster analysis of autoradiographic images. / Yin, K.; Zhao, Weizhao; Young, T. Y.; Ginsberg, Myron.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Vol. 2 Piscataway, NJ, United States : IEEE, 1999.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Nonparametric cluster analysis of autoradiographic images
AU - Yin, K.
AU - Zhao, Weizhao
AU - Young, T. Y.
AU - Ginsberg, Myron
PY - 1999/12/1
Y1 - 1999/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0033342330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0033342330&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0033342330
SN - 0780356756
VL - 2
BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PB - IEEE
CY - Piscataway, NJ, United States
ER -