Traditional neuroimaging experiments, dictated by the dogma of functional specialization, aim at identifying regions of the
brain that are maximally correlated with a simple cognitive or sensory stimulus. Very recently, functional MRI (fMRI) has
been used to infer subjective experience and brain states of subjects immersed in natural environments. These environments
are rich with uncontrolled stimuli and resemble real life experiences. Conventional methods of analysis of neuroimaging
data fail to unravel the complex activity that natural environments elicit. The contribution of this work is a novel method
to predict action and sensory experiences of a subject from fMRI. This method relies on an embedding that provides an
optimal coordinate system to reduce the dimensionality of the fMRI dataset while preserving its intrinsic dynamics. We
learn a set of time series that are implicit functions of the fMRI data, and predict the values of these times series in the future
from the knowledge of the fMRI data only. We conducted several experiments with the datasets of the 2007 Pittsburgh
Experience Based Cognition competition.
KEYWORDS: Wavelets, Functional magnetic resonance imaging, Hemodynamics, Signal to noise ratio, Associative arrays, In vivo imaging, Brain, Data modeling, Feature selection, Algorithm development
We address the problem of the analysis of event-related functional Magnetic Resonance Images (fMRI). We propose to separate the fMRI time series into "activated" and "non-activated" clusters. The fMRI time series are projected onto a basis, and the clustering is performed using the coefficients in that basis. We developed a new algorithm to select that basis which provides the optimal clustering of the time series. Our approach does not require any training datasets or any model of the hemodynamic response. The basis is constructed using a dictionary of wavelet packets. We search for the optimal basis in this dictionary using a new cost function that measures the clustering power of a set of wavelet packets. Our approach can be easily extended to classification problems. We have conducted several experiments with synthetic and in-vivo event-related fMRI data. Our method is capable of discovering the structures of the synthetic data. The method also successfully detected activated voxels in the in-vivo fMRI.
KEYWORDS: Functional magnetic resonance imaging, Wavelets, Data modeling, Signal detection, Brain mapping, Data centers, Signal processing, Wavelet transforms, Brain, Image segmentation
This work provides a new approach to estimate the parameters of a semi-parametric generalized linear model in the wavelet domain. The method is illustrated with the problem of detecting significant changes in fMRI signals that are correlated to a stimulus time course. The fMRI signal is described as the sum of two effects: a smooth trend and the response to the stimulus. The trend belongs to a subspace spanned by large scale wavelets. We have developed a scale space regression that permits to carry out the regression in the wavelet domain while omitting the scales that are contaminated by the trend. Experiments with fMRI data demonstrate that our approach can infer and remove drifts that cannot be adequately represented with low degree polynomials. Our approach results in a noticeable improvement by reducing the false positive rate and increasing the true positive rate.
KEYWORDS: Wavelets, Quantization, Data compression, Signal to noise ratio, Data acquisition, Multimedia, Image compression, Fourier transforms, Image processing, Data processing
The main drive behind the use of data compression in seismic data is the very large size of seismic data acquired. Some of the most recent acquired marine seismic data sets exceed 10 Tbytes, and in fact there are currently seismic surveys planned with a volume of around 120 Tbytes. Nevertheless, seismic data are quite different from the typical images used in image processing and multimedia applications. Some of their major differences are the data dynamic range exceeding 100 dB in theory, very often it is data with extensive oscillatory nature, the x and y directions represent different physical meaning, and there is significant amount of coherent noise which is often present in seismic data. The objective of this paper is to achieve higher compression ratio, than achieved with the wavelet/uniform quantization/Huffman coding family of compression schemes, with a comparable level of residual noise. The goal is to achieve above 40dB in the decompressed seismic data sets. One of the conclusions is that adaptive multiscale local cosine transform with different windows sizes performs well on all the seismic data sets and outperforms the other methods from the SNR point of view. Comparison with other methods (old and new) are given in the full paper. The main conclusion is that multidimensional adaptive multiscale local cosine transform with different windows sizes perform well on all the seismic data sets and outperforms other methods from the SNR point of view. Special emphasis was given to achieve faster processing speed which is another critical issue that is examined in the paper. Some of these algorithms are also suitable for multimedia type compression.
The goal of this work is to provide a new representation of functional magnetic resonance imaging (fMRI) time series. Functional neuroimaging aims at quantifying and localizing neuronal activity using imaging techniques. Functional MRI can detect and quantify hemodynamic changes induced by brain activation and neuronal activity. The time course of the fMRI signal at a given voxel inside the brain is represented with a structural model where each component of the model belongs to a subspace spanned by a small number of basis functions. The basis functions in different subspaces have very distinct time-frequency characteristics. The large scale trend of the signal is represented with a combination of large scale wavelets. The response to the stimulus is expanded on a small set of basis functions. Because it is critical to adapt the basis functions to the type of stimulus, the evoked response to a random presentation is expanded into small scale wavelets or wavelet packets, while the response to a periodic stimulus is represented with cosine or sine functions. We illustrate the estimation of the components of the model with several experiments.
Our goal in this paper is to provide a fast numerical implementation of the local trigonometric bases algorithm in order to demonstrate that an advantage can be gained by constructing a biorthogonal basis adapted to a target image. Different choices for the bells are proposed, and an extensive evaluation of the algorithm was performed on synthetic and seismic data. Because of its ability to reproduce textures so well, the coder performs very well, even at high bitrate.
The main contribution of this work is a new paradigm for image compression. We describe a new multi-layered representation technique for images. An image is encoded as the superposition of one main approximation, and a sequence of residuals. The strength of the multi-layered method comes from the fact that we use different bases to encode the main approximation and the residuals. For instance, we can use: a wavelet basis to encode a coarse main approximation of the image, wavelet packet bases to encode textured patterns, brushlet bases to encode localized oriented textured features, etc.
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