ABSTRACT
The Stockwell transform (ST), recently developed for geophysics, combines features of the Fourier, Gabor and wavelet transforms; it reveals frequency variation over time or space. This valuable information is obtained by Fourier analysis of a small segment of a signal at a time. Localization of the Fourier spectrum is achieved by filtering the signal with frequency-dependent Gaussian scaling windows. This multi-scale time-frequency analysis provides information about which frequencies occur and more importantly when they occur. Furthermore, the Stockwell domain can be directly inferred from the Fourier domain and vice versa. These features make the ST a potentially effective tool to visualize, analyze, and process medical imaging data. The ST has proven useful in noise reduction and tissue texture analysis. Herein, we focus on the theory and effectiveness of the ST for medical imaging. Its effectiveness and comparison with other linear time-frequency transforms, such as the Gabor and wavelet transforms, are discussed and demonstrated using functional magnetic resonance imaging data.
Subject(s)
Algorithms , Artifacts , Image Enhancement/methods , Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Motion , Signal Processing, Computer-Assisted , Brain/physiology , Evoked Potentials, Visual/physiology , Fourier Analysis , Humans , Respiration , Stochastic ProcessesABSTRACT
Observational evidence is presented in support of the hypothesis that large earthquakes excite the earth's natural wobble and produce the observed secular polar shift. Previous theoretical calculations based on elasticity theory and earthquake statistics had predicted a significant effect. There appear to be some premonitory signs of large earthquakes in the pole path.
