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A new local multiscale Fourier analysis for medical imaging
H Zhu1, B G Goodyear, M L Lauzon
1Department of Radiology, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, 1403-29th Street NW, Calgary, Alberta T2N 2T9, Canada. hzhu@ucalgary.ca
Medical Physics
|July 11, 2003
Summary
The Stockwell transform (ST) analyzes frequency changes over time, proving effective for medical imaging. This method enhances noise reduction and tissue analysis, outperforming other transforms in fMRI data.
Area of Science:
- Medical Imaging
- Signal Processing
- Geophysics
Background:
- The Stockwell transform (ST) integrates Fourier, Gabor, and wavelet transform features.
- It enables analysis of frequency variations across time or space.
- ST provides multi-scale time-frequency insights, detailing frequency occurrence and timing.
Purpose of the Study:
- To explore the theory and efficacy of the Stockwell transform in medical imaging.
- To compare ST with other linear time-frequency transforms like Gabor and wavelet transforms.
- To demonstrate ST's application using functional magnetic resonance imaging (fMRI) data.
Main Methods:
- Utilizing Fourier analysis on signal segments.
- Employing frequency-dependent Gaussian scaling windows for spectral localization.
- Direct inference between Stockwell and Fourier domains.
Main Results:
- ST demonstrates effectiveness in noise reduction and tissue texture analysis.
- The transform provides detailed time-frequency information crucial for medical data.
- ST shows potential for visualizing, analyzing, and processing medical images.
Conclusions:
- The Stockwell transform is a potent tool for medical imaging analysis.
- ST offers advantages over traditional transforms for specific medical imaging tasks.
- Its application in fMRI data analysis highlights its practical utility.
