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