DocumentCode :
3310068
Title :
Nonparametric cluster analysis of autoradiographic images
Author :
Yin, K. ; Zhao, W. ; Young, T.Y. ; Ginsberg, M.D.
Author_Institution :
Dept. of Electr. & Comput. Eng., Miami Univ., Coral Gables, FL, USA
Volume :
2
fYear :
1999
fDate :
36434
Abstract :
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 the Monte-Carlo method is also greatly reduced by a randomization method. An experiment compared differences in autoradiographic local blood flow in rats
Keywords :
Monte Carlo methods; biology computing; blood flow measurement; image recognition; image registration; medical image processing; radioisotope imaging; Monte-Carlo method; autoradiographic image data sets; autoradiographic images; cluster-size; computational burden; false positives; local blood flow; nonparametric cluster analysis; nonparametric statistical method; pixel-values; probability distribution; randomization method; rats; statistical parametric map; statistical power; thresholding; Blood flow; Data engineering; Image analysis; Pixel; Probability distribution; Rats; Scanning probe microscopy; Statistical distributions; Statistics; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint
Conference_Location :
Atlanta, GA
ISSN :
1094-687X
Print_ISBN :
0-7803-5674-8
Type :
conf
DOI :
10.1109/IEMBS.1999.804314
Filename :
804314
Link To Document :
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