Department of Mathematics and Statistics
Permanent URI for this collection
This collection contains pre-prints and post-prints of journal articles written by selected York University Mathematics faculty.
Browse
Browsing Department of Mathematics and Statistics by Author "Mitchell, Joseph"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Open Access Distributed Vector Processing of A New Local Multiscale: Fourier Transform for Medical Imaging Applications(IEEE Transactions on Medical Imaging, 2005) Brown, Robert; Zhu, Hongmei; Mitchell, JosephThe recently developed S-transform (ST) combines features of the Fourier andWavelet transforms; it reveals frequency variation over both space and time. It is a potentially powerful tool that can be applied to medical image processing including texture analysis and noise filtering. However, calculation of the ST is computationally intensive, making conventional implementations too slow for many medical applications. This problem was addressed by combining parallel and vector computations to provide a 25-fold reduction in computation time. This approach could help accelerate many medical image processing algorithms.Item Open Access Removal of phase artifacts from fMRI data using a Stockwell transform filter improves brain activity detection(Magnetic Resonance in Medicine, 2004) Goodyear, Bradley G.; Zhu, Hongmei; Brown, Robert A.; Mitchell, JosephA novel and automated technique is described for removing fMRI image artifacts resulting from motion outside of the imaging field of view. The technique is based on the Stockwell transform (ST), a mathematical operation that provides the frequency content at each time point within a time-varying signal. Using this technique, 1D Fourier transforms (FTs) are performed on raw image data to obtain phase profiles. The time series of phase magnitude for each and every point in the phase profile is then subjected to the ST to obtain a time-frequency spectrum. The temporal location of an artifact is identified based on the magnitude of a frequency component relative to the median magnitude of that frequency’s occurrence over all time points. After each artifact frequency is removed by replacing its magnitude with the median magnitude, an inverse ST is applied to regain the MR signal. Brain activity detection within fMRI datasets is improved by ignificantly reducing image artifacts that overlap anatomical regions of interest. The major advantage of ST-filtering is that artifact frequencies may be removed within a arrow time-window, while preserving the frequency information at all other time points.