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PubMed2005

Feature extraction from Doppler ultrasound signals for automated diagnostic systems.

This paper presented the assessment of feature extraction methods used in automated diagnosis of arterial diseases. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Different feature extraction methods were used to obtain feature vectors from ophthalmic and internal carotid arterial Doppler signals. In addition to this, the problem of selecting relevant features among the features available for the purpose of classification of Doppler signals was dealt with. Multilayer perceptron neural networks (MLPNNs) with different inputs (feature vectors) were used for diagnosis of ophthalmic and internal carotid arterial diseases. The assessment of feature extraction methods was performed by taking into consideration of performances of the MLPNNs. The performances of the MLPNNs were evaluated by the convergence rates (number of training epochs) and the total classification accuracies. Finally, some conclusions were drawn concerning the efficiency of discrete wavelet transform as a feature extraction method used for the diagnosis of ophthalmic and internal carotid arterial diseases.

Automation↗
PubMed1998

Time-frequency and time-scale techniques for the classification of native and bioprosthetic heart valve sounds.

The determination of diagnostic features in recorded heart sounds was investigated with Carpentier-Edwards (CE) bioprosthetic valves. Morphological features, extracted using the Choi-Williams distribution, achieved between 96 and 61% correct classification. The time-scale wavelet-transform feature set achieved 100% correct classification with native valve populations, and 87% with the CE replacements.

Aortic Valve↗
PubMed1998

Denoising of the uterine EHG by an undecimated wavelet transform.

We propose two original methods of denoising of the uterine electrohysterography (EHG) signal by wavelets. This external electrophysiological signal is corrupted by electronic, electromagnetic noises and by the remaining electrocardiogram of the mother. The interfering signals have overlapping spectra. Therefore, a classical filtering is unusable. Wavelets should be a very well-suited denoising tool. The first proposed method uses the algorithm "à trou" with nonsymmetrical filters. The computation is rapid and the results are satisfying compared to the classical denoising techniques. The second algorithm is an improvement of the first method. It uses orthogonal wavelets and the result of the thresholding corresponds to the average of all circulant shifts denoised by a decimated wavelet transform. Results are compared to traditional denoising algorithms by wavelet (orthogonal, maximally decimated). The proposed algorithms are more efficient on simulated signals as well as on uterine EHG.

Algorithms↗
PubMed2005

Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities.

A novel Bayesian nonparametric estimator in the Wavelet domain is presented. In this approach, a prior model is imposed on the wavelet coefficients designed to capture the sparseness of the wavelet expansion. Seeking probability models for the marginal densities of the wavelet coefficients, the new family of Bessel K forms (BKF) densities are shown to fit very well to the observed histograms. Exploiting this prior, we designed a Bayesian nonlinear denoiser and we derived a closed form for its expression. We then compared it to other priors that have been introduced in the literature, such as the generalized Gaussian density (GGD) or the alpha-stable models, where no analytical form is available for the corresponding Bayesian denoisers. Specifically, the BKF model turns out to be a good compromise between these two extreme cases (hyperbolic tails for the alpha-stable and exponential tails for the GGD). Moreover, we demonstrate a high degree of match between observed and estimated prior densities using the BKF model. Finally, a comparative study is carried out to show the effectiveness of our denoiser which clearly outperforms the classical shrinkage or thresholding wavelet-based techniques.

Algorithms↗
PubMed2005

Building robust wavelet estimators for multicomponent images using Stein's principle.

Multichannel imaging systems provide several observations of the same scene which are often corrupted by noise. In this paper, we are interested in multispectral image denoising in the wavelet domain. We adopt a multivariate statistical approach in order to exploit the correlations existing between the different spectral components. Our main contribution is the application of Stein's principle to build a new estimator for arbitrary multichannel images embedded in additive Gaussian noise. Simulation tests carried out on optical satellite images show that the proposed method outperforms conventional wavelet shrinkage techniques.

Algorithms↗
PubMed2005

Isotropic polyharmonic B-splines: scaling functions and wavelets.

In this paper, we use polyharmonic B-splines to build multidimensional wavelet bases. These functions are nonseparable, multidimensional basis functions that are localized versions of radial basis functions. We show that Rabut's elementary polyharmonic B-splines do not converge to a Gaussian as the order parameter increases, as opposed to their separable B-spline counterparts. Therefore, we introduce a more isotropic localization operator that guarantees this convergence, resulting into the isotropic polyharmonic B-splines. Next, we focus on the two-dimensional quincunx subsampling scheme. This configuration is of particular interest for image processing because it yields a finer scale progression than the standard dyadic approach. However, up until now, the design of appropriate filters for the quincunx scheme has mainly been done using the McClellan transform. In our approach, we start from the scaling functions, which are the polyharmonic B-splines and, as such, explicitly known, and we derive a family of polyharmonic spline wavelets corresponding to different flavors of the semi-orthogonal wavelet transform; e.g., orthonormal, B-spline, and dual. The filters are automatically specified by the scaling relations satisfied by these functions. We prove that the isotropic polyharmonic B-spline wavelet converges to a combination of four Gabor atoms, which are well separated in the frequency domain. We also show that these wavelets are nearly isotropic and that they behave as an iterated Laplacian operator at low frequencies. We describe an efficient fast Fourier transform-based implementation of the discrete wavelet transform based on polyharmonic B-splines.

Algorithms↗
PubMed2004

Recognition of affective prosody: continuous wavelet measures of event-related brain potentials to emotional exclamations.

The affective state of a speaker can be identified from the prosody of his or her speech. Voice quality is the most important prosodic cue for emotion recognition from short verbal utterances and nonverbal exclamations, the latter conveying pure emotion, void of all semantic meaning. We adopted two context violation paradigms-oddball and priming-to study the event-related brain potentials (ERP) reflecting this recognition process. We found a negative wave, the N300, in the ERPs to contextually incongruous exclamations, and interpreted this component as analogous to the well-known N400 response to semantically inappropriate words. The N300 appears to be a real-time psychophysiological measure of spontaneous emotion recognition from vocal cues, which could prove a useful tool for the examination of affective-prosody comprehension. In addition, we developed a new ERP component detection and estimation method that is based on the continuous wavelet transform (CWT), does not rely on visual inspection of the waveforms, and yields larger statistical difference effects than classical methods.

Adult↗
PubMed1997

Cardiac electrical instability of the arrhythmogenic tissue substrate assessed by means of wavelet transform.

The unstable activation wavefront from the tissue responsible for the production of ventricular tachycardia (VT) gives rise to beat-variable signals components that are eluded during the averaging step of high resolution ECG (HRECG). We used a mathematical model of small variations in a largely repetitive waveform to evaluate the beat-to-beat variance of the HRECG signal. The ability of the Morlet Wavelet Transform to discriminate the different factors responsible for the beat-variability (the alignment error and the beat-variable signal component) has been assessed on simulated signals. The performance evaluation on real ECG signals from normal subjects and patients with a documented history of ventricular tachycardia showed that the dynamics of the arrhythmogenic substrate as revealed by wavelet transform offers a significant improvement in ventricular tachycardia risk assessment.

Computer Simulation↗
PubMed2005

Mutual information-based analysis of JPEG2000 contexts.

Context-based arithmetic coding has been widely adopted in image and video compression and is a key component of the new JPEG2000 image compression standard. In this paper, the contexts used in JPEG2000 are analyzed using the mutual information, which is closely related to the compression performance. We first show that, when combining the contexts, the mutual information between the contexts and the encoded data will decrease unless the conditional probability distributions of the combined contexts are the same. Given I, the initial number of contexts, and F, the final desired number of contexts, there are S(I, F) possible context classification schemes where S(I, F) is called the Stirling number of the second kind. The optimal classification scheme is the one that gives the maximum mutual information. Instead of using an exhaustive search, the optimal classification scheme can be obtained through a modified generalized Lloyd algorithm with the relative entropy as the distortion metric. For binary arithmetic coding, the search complexity can be reduced by using dynamic programming. Our experimental results show that the JPEG2000 contexts capture the correlations among the wavelet coefficients very well. At the same time, the number of contexts used as part of the standard can be reduced without loss in the coding performance.

Algorithms↗
PubMed2000

Uterine EMG analysis: a dynamic approach for change detection and classification.

Toward the goal of detecting preterm birth by characterizing events in the uterine electromyogram (EMG), we propose a method of detection and classification of events in this signal. Uterine EMG is considered as a nonstationary signal and our approach consists of assuming piecewise stationarity and using a dynamic change detector with no a priori knowledge of the parameters of the hypotheses on the process state to be detected. The detection approach is based on the dynamic cumulative sum (DCS) of the local generalized likelihood ratios associated with a multiscale decomposition using wavelet transform. This combination of DCS and multiscale decomposition was shown to be very efficient for detection of both frequency and energy changes. An unsupervised classification based on the comparison between variance-covariance matrices computed from selected scales of the decomposition was implemented after detection. Finally a class labeling based on neural networks was developed. This algorithm of detection-classification-labeling gives satisfactory results on uterine EMG: in most cases more than 80% of the events are correctly detected and classified whatever the term of gestation.

Algorithms↗
PubMed2004

Evaluation of a wavelet-based computer-assisted detection system for identifying microcalcifications in digital full-field mammography.

PURPOSE: To evaluate a new wavelet-based computer-assisted detection (CAD) system for detecting and enhancing microcalcifications. MATERIAL AND METHODS: A total of 280 mammograms acquired by full-field digital mammography (Senographe 2000D; G.E. Medical Systems Milwaukee, Wisc., USA) were analyzed with and without a new wavelet-based CAD system for detecting and enhancing microcalcifications. The mammograms comprised roughly equal numbers of cases from each of the BIRADS (Breast Imaging, Reporting and Data System, according to the American College of Roentgenology) categories 1-5. Histologic confirmation was available for all of the 180 cases assigned BIRADS categories 3-5. Four readers interpreted all 280 images for suspicious microcalcifications using a scale of 1-5. The readers alternately assessed 5 images with and 5 without CAD. In a second reading immediately following the first, the readers had to reassess the 280 mammograms. The images that had already been interpreted without CAD were now presented with CAD and vice versa. The images were interpreted as soft copies on a diagnostic mammography workstation (Image Diagnost GmbH, Neufahrn/Munich, Germany). All images interpreted with CAD were presented with enhancement of microcalcifications by wavelet algorithms and prompting of microcalcifications. ROC (receiver operating characteristic) analyses were performed, and image interpretation time with and without CAD was measured. RESULTS: The overall time for interpretation required by all 4 readers together was 483 min with CAD compared to 580 min without CAD. ROC analysis revealed no significant advantage of CAD for the individual readers. Readers 3 (0.811/0.817) and 4 (0.799/0.843) had a slightly improved AUC (area under the curve) with CAD. Readers 1 and 2 had a slightly lower AUC with CAD (0.832 versus 0.861 and 0.818 versus 0.849). CONCLUSION: The CAD system significantly (P<0.05, t test) speeded up image interpretation with respect to the identification of microcalcifications, while the diagnostic quality remained almost identical under the study conditions.

Breast Diseases↗
PubMed2003

Steady-state visual evoked potentials in the low frequency range in migraine: a study of habituation and variability phenomena.

Previous studies have revealed that migraine patients display an increased photic driving to flash stimuli in the medium frequency range. The aim of this study was to perform a topographic analysis of steady-state visual evoked potentials (SVEPs) in the low frequency range (3-9 Hz), evaluating the temporal behaviour of the F1 amplitude by investigating habituation and variability phenomena. The main component of SVEPs, the F1, demonstrated an increased amplitude in several channels at 3 Hz. Behaviour of F1 amplitude was rather variable over time, and the wavelet-transform standard deviation was increased in migraine patients at a low stimulus rate. The discriminative value of the F1 mean amplitude and variability index, tested by both an artificial neural network classifier and a support vector machine, were high according to both methods. The increased photic driving in migraine should be subtended by a more generic abnormality of visual reactivity instead of a selective impairment of a visual subsystem. Temporal behaviour of SVEPs is not influenced by a clear tendency to habituation, but the F1 amplitude seemed to change in a complex way, which is better described by variability phenomena. An increased variability in response to flicker stimuli in migraine patients could be interpreted as an overactive regulation mechanism, prone to instability and consequently to headache attacks, whether spontaneous or triggered.

Adult↗
PubMed2003

Influence of cholinergic circuitries in generation of high-frequency somatosensory evoked potentials.

OBJECTIVE: High-frequency oscillations (HFOs) evoked by upper limb stimulation reflect highly synchronised spikes generated in the somatosensory human system. Since acetylcholine produces differential modulation in subgroups of neurons, we would determine whether cholinergic drive influences HFOs. METHODS: We recorded somatosensory evoked potentials (SEPs) from 31 scalp electrodes in 7 healthy volunteers, before and after single administration of rivastigmine, an inhibitor of central acetylcholinesterase. Right median nerve SEPs have been analysed after digital narrow bandpass filtering (500-700 Hz). Raw data were further submitted to Brain Electrical Source analysis (BESA) to evaluate the respective contribution of lemniscal, thalamic and cortical sources. Lastly, we analysed by Fast Fourier transform spectral changes after drug administration in the 10-30 ms latency range. RESULTS: Rivastigmine administration caused a significant increase of HFOs in the 18-28 ms latency range. Wavelets occurring before the onset latency of the conventional N20 SEP did not show any significant change. A similar increase concerned the strength of cortical dipolar sources in our BESA model. Lastly, we found a significant power increase of the frequency peak at about 600 Hz in P3-F3 traces after drug intake. CONCLUSIONS: Our findings demonstrate that the cortical component of HFOs is significantly enhanced by cholinergic activation. Pyramidal chattering cells, which are capable to discharge high-frequency bursts, are mainly modulated by cholinergic inputs; by contrast, acetylcholine does not modify the firing rate of fast-spiking GABAergic interneurons. We thus discuss the hypothesis that cortical HFOs are mainly generated by specialised pyramidal cells.

Acetylcholine↗
PubMed2003

Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering.

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data.

Algorithms↗
PubMed2002

Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients.

Colon cancer is the third leading class of cancer causing increased mortality in developed countries. A polyp is one type of lesion observed in a majority of colon cancer patients. Here, we report a microscopic Fourier transform infrared (FTIR) study of normal, adenomatous polyp and malignant cells from biopsies of 24 patients. The goal of our study was to differentiate an adenomatous polyp from a malignant cell using FTIR microspectroscopy and artificial neural network (ANN) analysis. FTIR spectra and biological markers such as phosphate, RNA/DNA derived from spectra, were useful in identifying normal cells from abnormal ones that consisted of adenomatous polyp and malignant cells. However, the biological markers failed to differentiate between adenomatous polyp and malignant cases. By employing a combination of wavelet features and an ANN based classifier, we were able to classify the different cells as normal, adenomatous polyp and cancerous in a given tissue sample. The percentage of success of classification was 89%, 81%, and 83% for normal, adenomatous polyp, and malignant cells, respectively. A comparison of the method proposed with the pathological method is also discussed.

Adenocarcinoma↗
PubMed1998

Spectral analysis methods for neurological signals.

This paper reviews some novel spectral analysis techniques that are useful for neurological signals in general and EEG signals in particular. First, some drawbacks and limitations of the commonly used Fast Fourier transforms (FFTs) are presented, and then alternative algorithms are outlined. An auto-regressive (AR) modeling based spectral estimation procedure is presented to overcome the problems of lower resolution and 'leakage' effects inherent in the FFT algorithm. For signals which are transient in nature or rapidly time-varying, two alternative algorithms are presented. The first is an adaptive AR parameter estimation algorithm and the second is a wavelet based time-frequency representation algorithm. Finally, a Spectral Distance measure and the Itakura distance measure are presented to quantify the differences between the spectra of two signals in a succinct manner. The application and performance of all the algorithms is illustrated using electroencephalograms (EEGs) recorded in animals during hypoxic asphyxic injury to brain.

Algorithms↗
PubMed2005

Myocardial motion estimation in tagged MR sequences by using alphaMI-based non rigid registration.

Tagged Magnetic Resonance Imaging (MRI) is currently the reference MR modality for myocardial motion and strain analysis. NMI-based non rigid registration has proven to be an accurate method to retrieve cardiac deformation fields. The use of alphaMI permits higher dimensional features to be implemented in myocardial deformation estimation through image registration. This paper demonstrates that this is feasible with a set of Haar wavelet features of high dimension. While we do not demonstrate performance improvement for this set of features, there is no significant degradation as compared to implementing the registration method with the traditional NMI metric. We use Entropic Spanning Graphs (ESGs) to estimate the alphaMI of the wavelet feature vectors WFVs since this is not possible with histograms. To the best of our knowledge, this is the first time that ESGs are used for non rigid registration.

Algorithms↗
PubMed2005

Detection of simulated lesions on data-compressed digital mammograms.

PURPOSE: To evaluate retrospectively the effect of a wavelet-based compression method on the detection of simulated masses of various sizes and clustered microcalcifications on data-compressed digital mammograms. MATERIALS AND METHODS: The images used in this study were acquired with institutional review board approval and patient informed consent, both of which allowed subsequent image data analysis. Patient identification was removed from images, and the study complied with requirements of the Health Insurance Portability and Accountability Act. Masses 3, 6, and 8 mm in diameter were analytically simulated and added to clinical mammographic backgrounds. In addition, microcalcifications were extracted from a clinical mammogram and hybridized with simulated microcalcifications for use in this study. Image compression conditions of 1:1, 15:1, and 30:1 were investigated. Observer responses were recorded with a six-point rating scale, and receiver operating characteristic (ROC) analysis was performed. In addition, two well-established numeric observer models were used to study the effect of image compression under the same compression conditions as were used with human observers. Analysis of variance was performed after observer adjustment to compare the mean values for area under the ROC curve (A(z)) across the three compression levels for the masses and microcalcification clusters. RESULTS: The results of the study indicated no significant differences in the A(z) values for masses with the compression conditions investigated. For images of microcalcifications, there were significant differences in A(z) values between compression ratios of 1:1 and 30:1 (P = .0005) and of 15:1 and 30:1 (P = .004); the difference between compression ratios of 1:1 and 15:1 was nonsignificant (P = .053). The observer models and human observers exhibited similar trends in detection of the masses investigated in this study. CONCLUSION: Detection of simulated masses was not affected by the compression method with the conditions used in this study, while the detection of microcalcifications was significantly reduced with a compression ratio of more than 15:1.

Analysis of Variance↗