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Activity Number:
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221
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305790 |
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Title:
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Signal Extraction in Noisy Images: Improvements to Wavelet-Based False Discovery Rate Methods
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Author(s):
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Joel O'Hair*+ and Wayne A. Woodward and Richard F. Gunst and William Schucany
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Companies:
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Southern Methodist University and Southern Methodist University and Southern Methodist University and Southern Methodist University
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Address:
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, , ,
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Keywords:
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Image Processing ; Signal Extraction ; FDR ; Wavelets ; fMRI ; EEG
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Abstract:
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When extracting a signal from a 2D image, standard hypothesis testing methods that control the False Discovery Rate (FDR) exhibit decreased power in the presence of correlated noise. One way of alleviating many of the problems related with correlated noise is testing for significance in the wavelet domain. Taking advantage of the wavelet transformation's decorrelating property and sparse representation, several powerful wavelet-based FDR methods have been developed. One difficulty with these methods is that decisions about the presence of signal at each point in the original domain are usually necessary. We will investigate techniques for making these spatial decisions after hypothesis testing on wavelet coefficients. The application of these techniques to images such as slices of fMRI statistic maps and EEG spectrograms will be examined.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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