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PubMed2006

Oriented wavelet transform for image compression and denoising.

In this paper, we introduce a new transform for image processing, based on wavelets and the lifting paradigm. The lifting steps of a unidimensional wavelet are applied along a local orientation defined on a quincunx sampling grid. To maximize energy compaction, the orientation minimizing the prediction error is chosen adaptively. A fine-grained multiscale analysis is provided by iterating the decomposition on the low-frequency band. In the context of image compression, the multiresolution orientation map is coded using a quad tree. The rate allocation between the orientation map and wavelet coefficients is jointly optimized in a rate-distortion sense. For image denoising, a Markov model is used to extract the orientations from the noisy image. As long as the map is sufficiently homogeneous, interesting properties of the original wavelet are preserved such as regularity and orthogonality. Perfect reconstruction is ensured by the reversibility of the lifting scheme. The mutual information between the wavelet coefficients is studied and compared to the one observed with a separable wavelet transform. The rate-distortion performance of this new transform is evaluated for image coding using state-of-the-art subband coders. Its performance in a denoising application is also assessed against the performance obtained with other transforms or denoising methods.

Algorithms↗
PubMed2005

Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing.

Since EEG is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. In this paper, we introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on artificial neural networks (ANNs). Logistic regression as well as feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used FFT and autoregressive (AR) model by using maximum likelihood estimation (MLE) of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or nonepileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying AR with MLE in connection with WNN, we obtained novel and reliable classifier architecture. The network is constructed by the error backpropagation neural network using Morlet mother wavelet basic function as node activation function. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The WNN-based classifier outperformed the FEBANN and logistic regression based counterpart. Within the same group, the WNN-based classifier was more accurate than the FEBANN-based classifier, and the logistic regression-based classifier.

Action Potentials↗
PubMed2000

Effect of CT digital image compression on detection of coronary artery calcification.

PURPOSE: To test the effect of digital compression of CT images on the detection of small linear or spotted high attenuation lesions such as coronary artery calcification (CAC). MATERIAL AND METHODS: Fifty cases with and 50 without CAC were randomly selected from a population that had undergone spiral CT of the thorax for screening lung cancer. CT image data were compressed using JPEG (Joint Photographic Experts Group) or wavelet algorithms at ratios of 10:1, 20:1 or 40:1. Five radiologists reviewed the uncompressed and compressed images on a cathode-ray-tube. Observer performance was evaluated with receiver operating characteristic analysis. RESULTS: CT images compressed at a ratio as high as 20:1 were acceptable for primary diagnosis of CAC. There was no significant difference in the detection accuracy for CAC between JPEG and wavelet algorithms at the compression ratios up to 20:1. CT images were more vulnerable to image blurring on the wavelet compression at relatively lower ratios, and "blocking" artifacts occurred on the JPEG compression at relatively higher ratios. CONCLUSION: JPEG and wavelet algorithms allow compression of CT images without compromising their diagnostic value at ratios up to 20:1 in detecting small linear or spotted high attenuation lesions such as CAC, and there was no difference between the two algorithms in diagnostic accuracy.

Adult↗
PubMed1995

Investigating the effects of opioid drugs on electrocortical activity using wavelet transform.

Fetal electrocortical activity (ECoG) is characterized by two distinct patterns: HVSA (high voltage, slow activity) and LVFA (low voltage, fast activity). Using the wavelet transform (WT), we recently reported that the frequency characteristics of these two ECoG patterns undergo significant maturational changes prior to birth (Akay et al. 1994a). We now report that fetal ECoG can also be significantly affected by pharmacological agents. In this paper, we compared the effects of two opioid drugs (morphine and [D-Pen2, D-Pen5]-enkephalin, DPDPE) on fetal ECoG, using the chronically instrumented fetal lamb model. Morphine was infused intravenously (i.v.) at 2.5 mg/h, while DPDPE was infused into the lateral cerebroventricle (i.c.v.) at 30 micrograms/h. The ECoG was analyzed using WT. We performed multi-resolution decomposition for four sets of parameters D2j where -1 < j < -4. The four series WTs represent the detail signal bandwidths: (1) 16-32 Hz, (2) 8-16 Hz, (3) 4-8 Hz, (4) 2-4 Hz. The data were subjected to statistical analysis using the Kolmogorov-Smirnov (KS) test. Both morphine and DPDPE resulted in a significant increase in power in the first wavelet band, while power was reduced in the second, third and fourth wavelet bands. In addition, both drugs resulted in a disruption of the normal cyclic pattern between the two ECoG patterns. There was a difference in the time course of action between morphine and DPDPE. This is the first occasion in which continuous ECoG has been subjected to rigorous statistical analysis. The results suggest that the WT-KS method is most suitable for quantitating changes in the ECoG induced by pharmacological agents.

Animals↗
PubMed1989

Localized texture processing in vision: analysis and synthesis in the Gaborian space.

Recent studies of cortical simple cell function suggest that the primitives of image representation in vision have a wavelet form similar to Gabor elementary functions (EF's). It is shown that textures and fully-textured images can be practically decomposed into, and synthesized from, a finite set of EF's. Textured-images can be synthesized from a set of EF's using image coefficient library. Alternatively, texturing of contoured (cartoon-like) images is analogous to adding chromaticity information to contoured images. A method for texture discrimination and image segmentation using local features based on the Gabor approach is introduced. Features related to the EF's parameters provide efficient means for texture discrimination and classification. This method is invariant under rotation and translation. The performance of the classification appears to be robust with respect to noisy conditions. The results show an insensitivity of the discrimination to relatively high noise levels, comparable to the performances of the human observer.

Artificial Intelligence↗
PubMed2006

Genome-wide DNA polymorphism analyses using VariScan.

BACKGROUND: DNA sequence polymorphisms analysis can provide valuable information on the evolutionary forces shaping nucleotide variation, and provides an insight into the functional significance of genomic regions. The recent ongoing genome projects will radically improve our capabilities to detect specific genomic regions shaped by natural selection. Current available methods and software, however, are unsatisfactory for such genome-wide analysis. RESULTS: We have developed methods for the analysis of DNA sequence polymorphisms at the genome-wide scale. These methods, which have been tested on a coalescent-simulated and actual data files from mouse and human, have been implemented in the VariScan software package version 2.0. Additionally, we have also incorporated a graphical-user interface. The main features of this software are: i) exhaustive population-genetic analyses including those based on the coalescent theory; ii) analysis adapted to the shallow data generated by the high-throughput genome projects; iii) use of genome annotations to conduct a comprehensive analyses separately for different functional regions; iv) identification of relevant genomic regions by the sliding-window and wavelet-multiresolution approaches; v) visualization of the results integrated with current genome annotations in commonly available genome browsers. CONCLUSION: VariScan is a powerful and flexible suite of software for the analysis of DNA polymorphisms. The current version implements new algorithms, methods, and capabilities, providing an important tool for an exhaustive exploratory analysis of genome-wide DNA polymorphism data.

Algorithms↗
PubMed2006

Permutation testing of orthogonal factorial effects in a language-processing experiment using fMRI.

The block-paradigm of the Functional Image Analysis Contest (FIAC) dataset was analysed with the Brain Activation and Morphological Mapping software. Permutation methods in the wavelet domain were used for inference on cluster-based test statistics of orthogonal contrasts relevant to the factorial design of the study, namely: the average response across all active blocks, the main effect of speaker, the main effect of sentence, and the interaction between sentence and speaker. Extensive activation was seen with all these contrasts. In particular, different vs. same-speaker blocks produced elevated activation in bilateral regions of the superior temporal lobe and repetition suppression for linguistic materials (same vs. different-sentence blocks) in left inferior frontal regions. These are regions previously reported in the literature. Additional regions were detected in this study, perhaps due to the enhanced sensitivity of the methodology. Within-block sentence suppression was tested post-hoc by regression of an exponential decay model onto the extracted time series from the left inferior frontal gyrus, but no strong evidence of such an effect was found. The significance levels set for the activation maps are P-values at which we expect <1 false-positive cluster per image. Nominal type I error control was verified by empirical testing of a test statistic corresponding to a randomly ordered design matrix. The small size of the BOLD effect necessitates sensitive methods of detection of brain activation. Permutation methods permit the necessary flexibility to develop novel test statistics to meet this challenge.

Algorithms↗
PubMed1997

Denoising of electron tomographic reconstructions using multiscale transformations.

The visualization of volume maps obtained by electron tomographic reconstruction is severely hampered by noise. As electron tomography is usually applied to individual, nonrepeatable structures, e.g., cell sections or cell organelles, the noise cannot be removed by averaging as is done implicitly in electron crystallography or explicitly in single particle analysis. In this paper, an approach for noise reduction is presented, based on a multiscale transformation, e.g., the wavelet transformation, in conjunction with a nonlinear filtration of the transform coefficients. After a brief introduction to the theoretical background, the effect of this type of noise reduction is demonstrated by test calculations as well as by applications to tomographic reconstructions of ice-embedded specimens. Regarding noise reduction and structure preservation, the method turns out to be superior to conventional filter techniques, such as the median filter or the Wiener filter. Results obtained with the use of different types of multiscale transformations are compared and the choice of suitable filter parameters is discussed.

Actins↗
PubMed2000

Opposite effects of myocardial stretch and verapamil on the complexity of the ventricular fibrillatory pattern: an experimental study.

An experimental model is used to analyze the effects of ventricular stretching and verapamil on the activation patterns during VF. Ten Langendorff-perfused rabbit hearts were used to record VF activity with an epicardial multiple electrode before, during, and after stretching with an intraventricular balloon, under both control conditions and during verapamil (Vp) infusion (0.4-0.8 mumol). The analyzed parameters were dominant frequency (FrD) spectral analysis, the median (MN) of the VF intervals, and the type of activation maps during VF (I = one wavelet without block lines, II = two simultaneous wavelets with block lines, III = three or more wavelets with block lines). Stretch accelerates VF (FrD: 22.8 +/- 6.4 vs 15.2 +/- 1.0 Hz, P < 0.01; MN: 48 +/- 13 vs 68 +/- 6 ms, P < 0.01). On fitting the FrD time changes to an exponential model after applying and suppressing stretch, the time constants (stretch: 101.2 +/- 19.6 s; stretch suppression: 97.8 +/- 33.2 s) do not differ significantly. Stretching induces a significant variation in the complexity of the VF activation maps with type III increments and type I and II decrements (control: I = 17.5%, II = 50.5%, III = 32%; stretch: I = 7%, II = 36.5%, III = 56.5%, P < 0.001). Vp accelerates VF (FrD: 20.9 +/- 1.9 Hz, P < 0.001 vs control; MN: 50 +/- 5 ms, P < 0.001 vs control) and diminishes activation maps complexity (I = 25.5%, II = 60.5%, III = 14%, P < 0.001 vs control). On applying stretch during Vp perfusion, the fibrillatory process is not accelerated to any greater degree. However, type I and II map decrements and type III increments are recorded, though reaching percentages similar to control (I = 16.5%, II = 53%, III = 30.5%, NS vs control). The following conclusions were found: (1) myocardial stretching accelerates VF and increases the complexity of the VF activation pattern; (2) time changes in the FrD of VF during and upon suppressing stretch fit an exponential model with similar time constants; and (3) although stretching and verapamil accelerate the VF process, they exert opposite effects upon the complexity of the fibrillatory pattern.

Animals↗
PubMed2006

Variable-length lapped transforms with a combination of multiple synthesis filter banks for image coding.

A class of lapped transforms for image coding, which are characterized by variable-length synthesis filters, is introduced. In this class, the synthesis filter bank (FB) is first defined with an arbitrary combination of finite impulse response synthesis filters of perfect reconstruction FBs. An analysis FB is then obtained using direct matrix inversion or iterative implementation of Neumann series expansion. Moreover, to improve compression, we introduce a unitary transform that follows the analysis FB. This class enables a greater freedom of design than previously presented variable-length lapped transforms. We illustrate several design examples and present experimental results for image coding, which indicate that the proposed transforms are promising and comparable with conventional subband transforms including wavelets.

Algorithms↗
PubMed2005

Automatic 4-D registration in dynamic MR renography based on over-complete dyadic wavelet and Fourier transforms.

Dynamic contrast-enhanced 4-D MR renography has the potential for broad clinical applications, but suffers from respiratory motion that limits analysis and interpretation. Since each examination yields at least over 10-20 serial 3-D images of the abdomen, manual registration is prohibitively labor-intensive. Besides in-plane motion and translation, out-of-plane motion and rotation are observed in the image series. In this paper, a novel robust and automated technique for removing out-of-plane translation and rotation with sub-voxel accuracy in 4-D dynamic MR images is presented. The method was evaluated on simulated motion data derived directly from a clinical patient's data. The method was also tested on 24 clinical patient kidney data sets. Registration results were compared with a mutual information method, in which differences between manually co-registered time-intensity curves and tested time-intensity curves were compared. Evaluation results showed that our method agreed well with these ground truth data.

Algorithms↗
PubMedDate not supplied

High-frequency periodic sources underlie ventricular fibrillation in the isolated rabbit heart.

The mechanism(s) underlying ventricular fibrillation (VF) remain unclear. We hypothesized that at least some forms of VF are not random and that high-frequency periodic sources of activity manifest themselves as spatiotemporal periodicities, which drive VF. Twenty-four VF episodes from 8 Langendorff-perfused rabbit hearts were studied using high-resolution video imaging in conjunction with ECG recordings and spectral analysis. Sequential wavefronts that activated the ventricles in a spatially and temporally periodic fashion were identified. In addition, we analyzed the lifespan and dynamics of wavelets in VF, using a new method of phase mapping that enables identification of phase singularity points (PSs), which flank individual wavelets. Spatiotemporal periodicity was found in 21 of 24 episodes. Complete reentry on the epicardial surface was observed in 3 of 24 episodes. The cycle length of discrete regions of spatiotemporal periodicity correlated highly with the dominant frequency of the optical pseudo-ECG (R(2)=0.75) and with the global bipolar electrogram (R(2)=0.79). The lifespan of PSs was short (14.7+/-14.4 ms); 98% of PSs existed for <1 rotation. The mean number of waves entering (6.50+/-0.69) exceeded the mean number of waves that exited our mapping field (4.25+/-0.56; P<0.05). These results strongly suggest that ongoing stable sources are responsible for the majority of the frequency content of VF and therefore play a role in its maintenance. In this model, multiple wavelets resulting from wavebreaks do not appear to be responsible for the sustenance of this arrhythmia, but are rather the consequence of breakup of high-frequency activation from a dominant reentrant source.

Animals↗
PubMed2003

A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio.

We present a method for the detection of action potentials, an essential first step in the analysis of extracellular neural signals. The low signal-to-noise ratio (SNR) and similarity of spectral characteristic between the target signal and background noise are obstacles to solving this problem and, thus, in previous studies on experimental neurophysiology, only action potentials with sufficiently large amplitude have been detected and analyzed. In order to lower the level of SNR required for successful detection, we propose an action potential detector based on a prudent combination of wavelet coefficients of multiple scales and demonstrate its performance for neural signal recording with varying degrees of similarity between signal and noise. The experimental data include recordings from the rat somatosensory cortex, the giant medial nerve of crayfish, and the cutaneous nerve of bullfrog. The proposed method was tested for various SNR values and degrees of spectral similarity. The method was superior to the Teager energy operator and even comparable to or better than the optimal linear detector. A detection ratio higher than 80% at a false alarm ratio lower than 10% was achieved, under an SNR of 2.35 for the rat cortex data where the spectral similarity was very high.

Action Potentials↗
PubMed2005

VariScan: Analysis of evolutionary patterns from large-scale DNA sequence polymorphism data.

SUMMARY: VeriScan is a software package for the analysis of DNA sequence polymorphisms at the whole genome scale. Among other features, the software (1) can conduct many population genetic analyses; (2) incorporates a multiresolution wavelet transform-based method that allows capturing relevant information from DNA polymorphism data; (3) facilitates the visualization of the results in the most commonly used genome browsers.

Algorithms↗
PubMed1999

Modeling the relationship between concurrent epicardial action potentials and bipolar electrograms.

A signal analysis approach to building the relationship between concurrent epicardial cell action potentials (AP's) and bipolar electrograms is presented. Wavelet network, one nonlinear black-box modeling method, is used to identify the relationship between cell AP's and bipolar electrocardiograms. The electrical signals were simultaneously measured from the epicardium of isolated Langendorff-perfused rabbit hearts during three different rhythm conditions: normal sinus rhythm (NSR), normal sinus rhythm after ischemia (NSRI), and ventricular fibrillation (VF). For NSR and NSRI, the proposed modeling method successfully captures the nonlinear input-output relationship and provides an accurate output, but the method fails in case of VF. This result suggests that a time-invariant nonlinear modeling method such as wavelet network is not appropriate for VF rhythm, which is thought to be time-varying as well as chaotic, but still useful in detection of VF. A new arrhythmia detection algorithm, with potential application in implantable devices, is proposed for identifying the time of rhythmic bifurcation.

Action Potentials↗
PubMed2001

Effects of JPEG and wavelet compression of spiral low-dose ct images on detection of small lung cancers.

PURPOSE: To compare the effect of compression of spiral low-dose CT images by the Joint Photographic Experts Group (JPEG) and wavelet algorithms on detection of small lung cancers. MATERIAL AND METHODS: Low-dose spiral CT images of 104 individuals (52 with peripheral lung cancers smaller than 20 mm and 52 control subjects) were used. The original images were compressed using JPEG or wavelet algorithms at a ratio of 10:1 or 20:1. Five radiologists interpreted these images and evaluated the image quality on a high-resolution CRT monitor. Observer performance was studied by receiver operating characteristic (ROC) analysis. RESULTS: There was no significant difference in the detection of cancers measuring 6 to 15 mm in uncompressed images and in those compressed by either of the algorithms, although the quality of images compressed at 20:1 with the wavelet algorithm was somewhat inferior. A lower diagnostic accuracy was noted using images compressed by the JPEG or wavelet algorithms at 20:1 in detecting lung cancers measuring 6 to 10 mm and cancers measuring from 6 to 15 mm with ground-glass opacity. CONCLUSION: Compression of low-dose CT images at a ratio of 10:1 using JPEG and wavelet algorithms does not compromise the detection rate of small lung cancers.

Algorithms↗
PubMed1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring.

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Evoked Potentials↗