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Effects of thresholding on correlation-based image similarity metrics.

Authors :
Sochat VV
Gorgolewski KJ
Koyejo O
Durnez J
Poldrack RA
Source :
Frontiers in neuroscience [Front Neurosci] 2015 Oct 29; Vol. 9, pp. 418. Date of Electronic Publication: 2015 Oct 29 (Print Publication: 2015).
Publication Year :
2015

Abstract

The computation of image similarity is important for a wide range of analyses in neuroimaging, from decoding to meta-analysis. In many cases the images being compared have empty voxels, but the effects of such empty voxels on image similarity metrics are poorly understood. We present a detailed investigation of the influence of different degrees of image thresholding on the outcome of pairwise image comparison. Given a pair of brain maps for which one of the maps is thresholded, we show that an analysis using the intersection of non-zero voxels across images at a threshold of Z = ±1.0 maximizes accuracy for retrieval of a list of maps of the same contrast, and thresholding up to Z = ±2.0 can increase accuracy as compared to comparison using unthresholded maps. Finally, maps can be thresholded up to to Z = ±3.0 (corresponding to 25% of voxels non-empty within a standard brain mask) and still maintain a lower bound of 90% accuracy. Our results suggest that a small degree of thresholding may improve the accuracy of image similarity computations, and that robust meta-analytic image similarity comparisons can be obtained using thresholded images.

Details

Language :
English
ISSN :
1662-4548
Volume :
9
Database :
MEDLINE
Journal :
Frontiers in neuroscience
Publication Type :
Academic Journal
Accession number :
26578875
Full Text :
https://doi.org/10.3389/fnins.2015.00418