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PubMed1999

Multiresolution analysis of emission tomography images in the wavelet domain.

This article develops a theoretical framework for the use of the wavelet transform in the estimation of emission tomography images. The solution of the problem of estimation addresses the equivalent problems of optimal filtering, maximum compression, and statistical testing. In particular, new theory and algorithms are presented that allow current wavelet methodology to deal with the two main characteristics of nuclear medicine images: low signal-to-noise ratios and correlated noise. The technique is applied to synthetic images, phantom studies, and clinical images. Results show the ability of wavelets to model images and to estimate the signal generated by cameras of different resolutions in a wide variety of noise conditions. Moreover, the same methodology can be used for the multiscale analysis of statistical maps. The relationship of the wavelet approach to current hypothesis-testing methods is shown with an example and discussed. The wavelet transform is shown to be a valuable tool for the numerical treatment of images in nuclear medicine. It is envisaged that the methods described here may be a starting point for further developments in image reconstruction and image processing.

Algorithms↗
PubMed2000

Performance analysis of a new semiorthogonal spline wavelet compression algorithm for tonal medical images.

Lossy image compression is thought to be a necessity as radiology moves toward a filmless environment. Compression algorithms based on the discrete cosine transform (DCT) are limited due to the infinite support of the cosine basis function. Wavelets, basis functions that have compact or nearly compact support, are mathematically better suited for decorrelating medical image data. A lossy compression algorithm based on semiorthogonal cubic spline wavelets has been implemented and tested on six different image modalities (magnetic resonance, x-ray computed tomography, single photon emission tomography, digital fluoroscopy, computed radiography, and ultrasound). The fidelity of the reconstructed wavelet images was compared to images compressed with a DCT algorithm for compression ratios of up to 40:1. The wavelet algorithm was found to have generally lower average error metrics and higher peak-signal-to-noise ratios than the DCT algorithm.

Algorithms↗
PubMed2002

Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage.

In the field of template-based medical image analysis, image registration and normalization are frequently used to evaluate and interpret data in a standard template or reference atlas space. Despite the large number of image-registration (warping) techniques developed recently in the literature, only a few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper, we introduce a new approach for analyzing image registration based on a selective-wavelet reconstruction technique using a frequency-adaptive wavelet shrinkage. We study four polynomial-based and two higher complexity nonaffine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging) and functional (positron emission tomography) data. Depending upon the aim of the image registration, we present several warp classification schemes. Our method uses a concise representation of the native and resliced (pre- and post-warp) data in compressed wavelet space to assess quality of registration. This technique is computationally inexpensive and utilizes the image compression, image enhancement, and denoising characteristics of the wavelet-based function representation, as well as the optimality properties of frequency-dependent wavelet shrinkage.

Brain↗
PubMed2004

Dissociating the spatio-temporal characteristics of cortical neuronal activity associated with human volitional swallowing in the healthy adult brain.

Human swallowing represents a complex highly coordinated sensorimotor function whose functional neuroanatomy remains incompletely understood. Specifically, previous studies have failed to delineate the temporo-spatial sequence of those cerebral loci active during the differing phases of swallowing. We therefore sought to define the temporal characteristics of cortical activity associated with human swallowing behaviour using a novel application of magnetoencephalography (MEG). In healthy volunteers (n = 8, aged 28-45), 151-channel whole cortex MEG was recorded during the conditions of oral water infusion, volitional wet swallowing (5 ml bolus), tongue thrust or rest. Each condition lasted for 5 s and was repeated 20 times. Synthetic aperture magnetometry (SAM) analysis was performed on each active epoch and compared to rest. Temporal sequencing of brain activations utilised time-frequency wavelet plots of regions selected using virtual electrodes. Following SAM analysis, water infusion preferentially activated the caudolateral sensorimotor cortex, whereas during volitional swallowing and tongue movement, the superior sensorimotor cortex was more strongly active. Time-frequency wavelet analysis indicated that sensory input from the tongue simultaneously activated caudolateral sensorimotor and primary gustatory cortex, which appeared to prime the superior sensory and motor cortical areas, involved in the volitional phase of swallowing. Our data support the existence of a temporal synchrony across the whole cortical swallowing network, with sensory input from the tongue being critical. Thus, the ability to non-invasively image this network, with intra-individual and high temporal resolution, provides new insights into the brain processing of human swallowing.

Adult↗
PubMed1999

Time-scale analysis of motor unit action potentials.

Quantitative analysis in clinical electromyography (EMG) is very desirable because it allows a more standardized, sensitive and specific evaluation of the neurophysiological findings, especially for the assessment of neuromuscular disorders. Following the recent development of computer-aided EMG equipment, different methodologies in the time domain and frequency domain have been followed for quantitative analysis. In this study, the usefulness of the wavelet transform (WT), that provides a linear time-scale representation is investigated, for describing motor unit action potential (MUAP) morphology. The motivation behind the use of the WT is that it provides localized statistical measures (the scalogram) for nonstationary signal analysis. The following four WT's were investigated in analyzing a total of 800 MUAP's recorded from 12 normal subjects, 15 subjects suffering with motor neuron disease, and 13 from myopathy: Daubechies with four and 20 coefficients, Chui (CH), and Battle-Lemarie (BL). The results are summarized as follows: 1) most of the energy of the MUAP signal is distributed among a small number of well-localized (in time) WT coefficients in the region of the main spike, 2) for MUAP signals, we look to the low-frequency coefficients for capturing the average waveshape of the MUAP signal over long durations, and we look to the high-frequency coefficients for locating MUAP spike changes, 3) the Daubechies 4 wavelet, is effective in tracking the transient components of the MUAP signal, 4) the linear spline CH (semiorthogonal) wavelet provides the best MUAP signal approximation by capturing most of the energy in the lowest resolution approximation coefficients, and 5) neural network DY (DY) of Daubechies 4 and BL WT coefficients was in the region of 66%, whereas DY for the empirically determined time domain feature set was 78%. In conclusion, wavelet analysis provides a new way in describing MUAP morphology in the time-frequency plane. This method allows for the fast extraction of localized frequency components, which when combined with time domain analysis into a modular neural network decision support system enhances further the DY to 82.5% aiding the neurophysiologist in the early and accurate diagnosis of neuromuscular disorders.

Action Potentials↗
PubMed2001

Suprathreshold wavelet coefficient quantization in complex stimuli: psychophysical evaluation and analysis.

A psychophysical experiment is described that quantifies human sensitivities to suprathreshold distortions caused by wavelet coefficient quantization in natural images, and the resulting analysis is explained. The quantizer step sizes that cause the first three visible degradations relative to the original image are well predicted by exponential functions of subband standard deviation. The resulting root-mean-square (RMS) error in the image is constant for a spatial frequency and is independent of orientation. Contrast sensitivity calculations suggest a higher sensitivity to bands with higher energy, and threshold elevations for the second and third visible degradations are predicted well by the constant-RMS model. A quantization strategy based on the results is proposed for low-bit-rate applications.

Contrast Sensitivity↗
PubMed1999

Nonlinear analysis of the performance and reliability of wavelet singularity detection based denoising for Doppler ultrasound fetal heart rate signals.

Many studies on the physiology of the cardiovascular system revealed that nonlinear chaotic dynamics govern the generation of the heart rate signal. This is also valid for the fetal heart rate (FHR) variability, where however the variability is affected by many more factors and is significantly more complicated than for the adult case. Recently an adaptive wavelet denoising method for the Doppler ultrasound FHR recordings has been introduced. In this paper the performance and reliability of that method is confirmed by the observation that for the wavelet denoised FHR signal, a deterministic nonlinear structure, which was concealed by the noise, becomes apparent. It provides strong evidence that the denoising process removes actual noise components and can therefore be utilized for the improvement of the signal quality. Hence by observing after denoising a significant improvement of the 'chaoticity' of the FHR signal we obtain strong evidence for the reliability and efficiency of the wavelet based denoising method. The estimation of the chaoticity of the FHR signal before and after the denoising is approached with three nonlinear analysis methods. First, the rescaled scale analysis (RSA) technique reveals that the denoising process increases the Hurst exponent parameter as happens when additive noise is removed from a chaotic signal. Second, the nonlinear prediction error evaluated with radial basis function (RBF) prediction networks is significantly lower at the denoised signal. The significant gain in predictability can be attributed to the drastic reduction of the additive noise from the signal by the denoising algorithm. Moreover, the evaluation of the correlation coefficient between actual and neural network predicted values as a function of the prediction time displays characteristics of chaos only for the denoised signal. Third, a chaotic attractor, reconstructed with the embedding dimension technique, becomes evident for the denoised signal, while it is completely obscured for the original signals. The correlation dimension of the reconstructed attractor for the denoised signal tends to reach a value independent of the embedding dimension, a sign of deterministic chaotic signal. In contrast for the original signal the correlation dimension increases steadily with the embedding dimension, a fact that indicates strong contribution of noise.

Adult↗
PubMed2004

Multiscale approximation with hierarchical radial basis functions networks.

An approximating neural model, called hierarchical radial basis function (HRBF) network, is presented here. This is a self-organizing (by growing) multiscale version of a radial basis function (RBF) network. It is constituted of hierarchical layers, each containing a Gaussian grid at a decreasing scale. The grids are not completely filled, but units are inserted only where the local error is over threshold. This guarantees a uniform residual error and the allocation of more units with smaller scales where the data contain higher frequencies. Only local operations, which do not require any iteration on the data, are required; this allows to construct the network in quasi-real time. Through harmonic analysis, it is demonstrated that, although a HRBF cannot be reduced to a traditional wavelet-based multiresolution analysis (MRA), it does employ Riesz bases and enjoys asymptotic approximation properties for a very large class of functions. HRBF networks have been extensively applied to the reconstruction of three-dimensional (3-D) models from noisy range data. The results illustrate their power in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by MRA.

Face↗
PubMed2000

Modeling dynamic PET-SPECT studies in the wavelet domain.

This work develops a theoretical framework and corresponding algorithms for the modeling of dynamic PET-SPECT studies both in time and space. The problem of estimating the spatial dimension is solved by applying the wavelet transform to each scan of the dynamic sequence and then performing the kinetic modeling and statistical analysis in the wavelet domain. On reconstruction through the inverse wavelet transform, one obtains parametric images that are consistent estimates of the spatial patterns of the kinetic parameter of interest. The theoretical setup allows the use of linear techniques currently used in PET-SPECT for kinetic analysis. The method is applied to artificial and real data sets. The application to dynamic PET-SPECT studies was performed both for validation purposes, when the spatial patterns are known, and for illustration of the advantages offered by the technique in case of tracers with an unknown pattern of distribution.

Benzamides↗
PubMed1999

[Analysis for the auditory brainstem responses of guinea pigs with wavelet transform].

OBJECTIVE: To fine out the advantages of wavelet transform when it is applied in analysis of auditory brainstem responses. METHOD: Thirty of male, albino guinea pigs weighing 300 to 400 g were divided into three groups: 1. normal hearing; 2. conductive hearing loss; 3. sensorineural hearing loss. ABR of the animals was collected and represented with wavelet transform respectively. The signals before and after being transferred was contrasted and dealt with statistics. RESULT: The ABR signals are distorted in wavelet transform scale 2 and background noises are increased in scale 4. The conventional time feature of ABR signals can be conserved and the present of I wave can be increased in wavelet transform scale 3. CONCLUSION: Wavelet transform scale 3 is proper to analyze the ABR signals of guinea pig. Compared with conventional method, it can conserve the ABR signal's time feature and increase the emerged rate of I wave.

Animals↗
PubMedDate not supplied

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain.

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.

Algorithms↗
PubMedDate not supplied

Proton nuclear magnetic resonance spectroscopy of plasma lipoprotein: technical problems and potential interest in cancer disease.

This paper reviews several methods presently available for analysing lipoprotein NMR spectra. Two main steps can be distinguished: NMR signal processing and data analysis. Time domain (wavelet transform) and frequency domain (curve fitting) signal processing methods are compared. Statistical methods of data analysis (Ascending Hierarchical Classification, Correspondence Analysis and Principal Component Analysis) have been tested on simulated NMR data of plasma lipoprotein with different numbers of sampling points and different noise levels. These few examples clearly attest that the NMR approach to complex "mixture" (such as body fluids) analysis is emerging from its infancy. New interest in plasma lipoprotein analysis in cancer biology is finally discussed in the light of previous clinical and experimental results and of understanding of lipid metabolism in cancer.

Humans↗
PubMed2004

Automated morphometric analysis of the cat retinal alpha/Y, beta/X and delta ganglion cells using wavelet statistical moment and clustering algorithms.

Computational morphological analysis comprises the development of measures (indicators) that describe different form attributes of a neuron and provides additional parameters for classification algorithms. Our work addressed the problem of small group sizes often encountered in neuromorphological and neurophysiological research, automated classification tasks (unsupervised learning) and introduced a new morphological measure: the wavelet statistical moment. We analysed cat alpha/Y, beta/X and delta Golgi-stained retinal ganglion cells using six different shape features (circularity, 2(nd) statistical moment and entropy of Gaussian blurred images, wavelet statistical moment, number of terminations and the fractal dimension). This allowed us to compare the sensitivity of the methods in uniquely describing morphological attributes of these cells.

Algorithms↗
PubMed1999

Classifying mammographic mass shapes using the wavelet transform modulus-maxima method.

In this article, multiresolution analysis, specifically the discrete wavelet transform modulus-maxima (mod-max) method, is utilized for the extraction of mammographic mass shape features. These shape features are used in a classification system to classify masses as round, nodular, or stellate. The multiresolution shape features are compared with traditional uniresolution shape features for their class discriminating abilities. The study involved 60 digitized mammographic images. The masses were segmented manually by radiologists, prior to introduction to the classification system. The uniresolution and multiresolution shape features were calculated using the radial distance measure of the mass boundaries. The discriminating power of the shape features were analyzed via linear discriminant analysis (LDA). The classification system utilized a simple Euclidean metric to determine class membership. The system was tested using the apparent and leave-one-out test methods. The classification system when using the multiresolution and uniresolution shape features resulted in classification rates of 83% and 80% for the apparent and leave-one-out test methods, respectively. In comparison, when only the uniresolution shape features were used, the classification rates were 72 and 68% for the apparent and leave-one-out test methods, respectively.

Breast Neoplasms↗
PubMedDate not supplied

Relationship between time- and frequency-domain analyses of angular head movements in the squirrel monkey.

We used the three-dimensional magnetic search-coil recording technique to study the range of active angular head movements made by squirrel monkeys. There were two goals in this study: (1) to determine the range of angular velocities and accelerations as well as the bandwidth and other frequency characteristics of active head movements and (2) to compare analyses of transients of velocity and acceleration that are studied by residual analysis, Fourier transform, and wavelet transform of the head velocity signal. The residual analysis showed that the shape and duration of the transients affected the bandwidth. During the time after the head had begun to accelerate, the frequency content of the head movement extended into the range of 6 to 12 Hz. When considering all three planes of rotation, approximately 75% of the transients had peak acceleration between 2,000 and 10,000 deg/s(2) and a peak velocity of 50 to 400 deg/s. A peak acceleration of >10,000 deg/s(2) was recorded in 10% of the transients. These findings indicate that active head movements in squirrel monkeys cover a higher range of frequencies, accelerations, and velocities than have typically been used in previous eye-movement and neuronal studies of the reflexes that control gaze. We further conclude that the choice of a method for analyzing transient, time-varying biological signals is dependent on the desired information. Residual analysis provides detailed resolution in the time domain, but estimation of the frequency content of the signal is dependent on the portions selected for analysis and the choice of filters. Fourier transform provides a representation of the power spectrum in the frequency domain but without any inherent temporal resolution. We show that the wavelet transform, a novel method as applied to the signal analysis goals of this study, is the most useful technique for relating time- and frequency-domain information during a continuous signal.

Acceleration↗