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 (US) |
|---|---|
| Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Publisher | IEEE |
| Number of pages | 1 |
| 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 |
Publication series
| Name | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
|---|---|
| Volume | 2 |
| ISSN (Print) | 0589-1019 |
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 |
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
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Cite this
Yin, K., Zhao, W., Young, T. Y., & Ginsberg, M. D. (1999). Nonparametric cluster analysis of autoradiographic images. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 2). IEEE.