Paper Info
Title
Structured noise analysis in intrinsic optical imaging
Abstract
In this paper, a novel structured noises-reduction technique for OI data is proposed. Canonical correlation analysis (CCA) technique is exploited to separate the underlying independent sources among which the neural response signal is picked out by the correlation analysis. The white noise (WN) criterion is applied to discern the structured components from the unstructured ones. The energy of structured noises is then eliminated from the original data. Monte Carlo simulation is used to test the validity of the procedure. The result shows that after the noise reduction, the true positive rate improves significantly without raising the false positive rate. Five sets of OI data of single trial collected from the HP area of rat's cortex are processed by the procedure and the resulting activation maps present more detailed spatial architecture than those without noise reduction.
Year
DOI
Venue
2010
10.1109/COGINF.2010.5599711
IEEE ICCI
Keywords
Field
DocType
canonical correlation analysis(cca),optical image (oi),monte carlo simulation,neural response signal,intrinsic optical imaging,neurophysiology,biomedical optical imaging,white noise criterion,structured noise analysis,image denoising,noise reduction,monte carlo methods,spatial architecture,brain,white noise,canonical correlation analysis,correlation methods,medical image processing,activation maps,false positive rate,correlation,optical imaging,pixel,signal to noise ratio,principal component analysis
Noise reduction,Computer vision,False positive rate,Monte Carlo method,Pattern recognition,Canonical correlation,Computer science,Signal-to-noise ratio,White noise,Artificial intelligence,Pixel,Principal component analysis
Conference
Volume
Issue
ISBN
null
null
978-1-4244-8041-8
Citations 
PageRank 
References 
0
0.34
4
Authors
6
Name
Order
Citations
PageRank
Hai-bing Yin19018.64
Yadong Liu210514.04
Zongtan Zhou341233.89
Ming Li4134.67
Yucheng Wang5112.23
Dewen Hu61290101.20