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PubMed1999

Classification of tetraplegics through automatic movement evaluation.

The general problem of classification of functional movements in humans with spinal cord injuries requires the following questions to be answered: what are the essential kinematic parameters that we have to observe during the movement? Is it possible to estimate preserved motor skills based on kinematics? Which computational method for identification is suited to geometric feature analysis? To answer these questions we have developed the methodology which has two phases: (1) recordings of a series of specified arm movements; and (2) custom made software for graphical presentation of arm movements and the design of wavelet and neural networks for movement classification. The proposed protocol is automated and both graphical presentation and neural networks allow easy interpretation of the instrumented assessment to accomplish automatic classification of arm movements in tetraplegics. The protocol was evaluated on 16 spinal cord injury (SCI) patients and seven healthy control subjects for three different arm movements. The classification rate yielded results in the range 46-100% for movement trials that were tested. The application of neural networks for classification of arm movements is completed with results using different neural networks: backpropagation, radial basis, recurrent (Elman), self-organizing and Learning Vector Quantization (LVQ).

Adolescent↗
PubMed2003

Computer-aided tumor detection in endoscopic video using color wavelet features.

We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.

Adenomatous Polyps↗
PubMed2001

Cephalometric image analysis and measurement for orthognathic surgery.

Automatic identification of landmarks in cephalometry is very important and useful for orthognathic surgery. A computerised automatic cephalometric analysis system (CACAS), based on image processing, is presented. For an original X-ray image, median filtering and histogram equalisation are used to improve image quality. The edge of an X-ray image is detected by a wavelet transform and Canny filter. Seventeen landmarks in cephalometry are successfully identified by knowledge-based edge tracing and changeable templates. Seventy-three measurements based on distances, angles and ratios between landmarks are computed automatically. The reliability of the landmarks and the validity of the measurements are compared for automatic and manual operation. The values of measurements obtained by CACAS are more precise and reliable: the mean error for linear measurements is less than 0.9mm; the mean error for angular measurements is less than 1.2 degrees. The rate of validity is over 80%, even if the image quality is poor. For an image with a high signal-to-noise ratio, the rate of validity of landmarking and measurements using the CACAS system is over 90%.

Cephalometry↗
PubMed2006

Fractal characteristics of human Parkinsonian neuronal spike trains.

Fractal analysis was applied to human pallidal neuronal spike trains recorded from patients with Parkinson's disease during ablative surgery of the internal segment of the globus pallidus. Fractal dynamics was quantified by computing the scaling exponent with the average wavelet coefficient approach. We observed fractal persistent correlation in the fluctuation of the interspike intervals of neuronal spike trains recorded in the internal segment of the globus pallidus both before and after the administration of dopamine agonist apomorphine. However, there was a significant increase in the scaling exponent during the "on" state after apomorphine administration as compared with the parkinsonian "off" state prior to apomorphine. In addition, we observed a statistically significant decrease in the average firing rate in the transition from the "off" to the "on" state. We conclude that robust fractal dynamics can be observed in single neurons in the human CNS, indicating that human neuronal dynamics of the internal segment of the globus pallidus are essentially a nonlinear and nonequilibrium process, with a long-range correlation or memory extending across many time scales. Accompanying the "on" state after apomorphine administration was an improvement in the long-range persistent correlation as compared with the more random dynamics in the "off" state. A scaling exponent signaling a breakdown or modification in long-range correlation in a single neuron may serve as a useful indicator of a dysfunctional network in the human CNS.

Apomorphine↗
PubMed2000

Clinical evaluation of wavelet-compressed digitized screen-film mammography.

RATIONALE AND OBJECTIVES: The authors compared diagnostic accuracy and callback rates with conventional screen-film mammograms and wavelet-compressed digitized images. MATERIALS AND METHODS: Sixty sets of mammograms (four views per case) were digitized at a spatial resolution of 100 microm. The images were wavelet compressed to a mean compression ratio of 8:1 and reviewed by three mammographers. Five regions were evaluated in each breast. Suspicion of malignancy was graded on a scale of 0% to 100%, and receiver operating characteristic (ROC) analysis was performed. Callback rates were calculated by using the American College of Radiology's Breast Imaging Reporting and Data System lexicon scale. RESULTS: The mean diagnostic accuracy with compressed and conventional images was 0.832 and 0.860, respectively. The upper 95% confidence bound for the difference in ROC areas was 0.061. The mean false-positive rate at a fixed sensitivity of 0.90 was 0.041 for compressed images and 0.059 for conventional images. The mean callback rates for normal, benign, and malignant regions were 0.023, 0.305, and 0.677, respectively, for compressed images and 0.036, 0.447, and 0.750, respectively, for conventional images. The upper 95% confidence bound for the (absolute) differences in callback rates was 0.012 for normal regions, 0.163 for benign regions, and 0.138 for malignant regions. CONCLUSION: Diagnostic accuracies were equivalent for both compressed and conventional images. The mean false-positive rate at fixed sensitivity was much better with the compressed images. However, the callback rates for malignant lesions were lower when the compressed images were used.

Breast Diseases↗
PubMed1999

A method for the automatic detection of arousals during sleep.

A method for the automatic detection of arousals in digital polysomnographic recordings is described. The computer program analyzed two EEG and one EMG derivations marking variable length segments as arousals. The processing of EEG data started from the wavelet transform, which characterizes the signal in the time-frequency domain, and resulted in a set of indices used to discriminate possible arousal segments. Transient increases in muscle activity were also identified, while a multichannel and context sensitive analysis allowed arousal detection. Out of 11 overnight recordings, 3 were used as the training set and 8 as the program testing set. In the first stage of the study two EEG experts inspected the tracings independently to score arousals. They then reviewed all recordings and jointly examined each event for validation, both those scored by themselves and those scored by the computer. A reference set of definite arousals (1125 in the testing set) and a number of uncertain events (266) were thus obtained. The sensitivity of the automatic system (88.1%) was higher than that of the human experts (72.4 and 78.4%) while the selectivity was lower (74.5% for the automatic system, 83.0 and 82.0% for the experts). This suggested that automatic detection, followed by an expert's validation, may render the analysis of arousals more widely feasible as well as support the study of arousal features.

Adult↗
PubMed2005

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models.

In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs). Neural activation detection is formulated at the voxel level in terms of time coupling between the sequence of hemodynamic response onsets (HROs) observed in the fMRI signal, and an HSMESM of the hidden sequence of task-induced neural activations. The sequence of HRO events is derived from a continuous wavelet transform (CWT) of the fMRI signal. The brain activation HSMESM is built from the timing information of the input stimulation protocol. The rich mathematical framework of HSMESMs makes these models an effective and versatile approach for fMRI data analysis. Solving for the HSMESM Evaluation and Learning problems enables the model to automatically detect neural activation embedded in a given set of fMRI signals, without requiring any template basis function or prior shape assumption for the fMRI response. Solving for the HSMESM Decoding problem allows to enrich brain mapping with activation lag mapping, activation mode visualizing, and hemodynamic response function analysis. Activation detection results obtained on synthetic and real epoch-related fMRI data demonstrate the superiority of the HSMESM mapping method with respect to a real application case of the statistical parametric mapping (SPM) approach. In addition, the HSMESM mapping method appears clearly insensitive to timing variations of the hemodynamic response, and exhibits low sensitivity to fluctuations of its shape.

Algorithms↗
PubMed2006

Facial expression recognition using kernel canonical correlation analysis (KCCA).

In this correspondence, we address the facial expression recognition problem using kernel canonical correlation analysis (KCCA). Following the method proposed by Lyons et al. and Zhang et al., we manually locate 34 landmark points from each facial image and then convert these geometric points into a labeled graph (LG) vector using the Gabor wavelet transformation method to represent the facial features. On the other hand, for each training facial image, the semantic ratings describing the basic expressions are combined into a six-dimensional semantic expression vector. Learning the correlation between the LG vector and the semantic expression vector is performed by KCCA. According to this correlation, we estimate the associated semantic expression vector of a given test image and then perform the expression classification according to this estimated semantic expression vector. Moreover, we also propose an improved KCCA algorithm to tackle the singularity problem of the Gram matrix. The experimental results on the Japanese female facial expression database and the Ekman's "Pictures of Facial Affect" database illustrate the effectiveness of the proposed method.

Algorithms↗
PubMed2000

Wavelet compression on detection of brain lesions with magnetic resonance imaging.

The purpose of this report is to assess clinically acceptable compression ratios on the detection of brain lesions at magnetic resonance imaging (MRI). Four consecutive T2-weighted and the corresponding T1-weighted images obtained in 20 patients were studied for 109 anatomic sites including 50 with lesions and 59 without lesions. The images were obtained on a 1.5-T MR unit with a pixel size of 0.9 to 1.2 x 0.47 mm and a section thickness of 5 mm. The image data were compressed by wavelet-based algorithm at ratios of 20:1, 40:1, and 60:1. Three radiologists reviewed these images on an interactive workstation and rated the presence or absence of a lesion with a 50 point scale for each anatomic site. The authors also evaluated the influence of pixel size on the quality of image compression. At receiver operating characteristic (ROC) analysis, no statistically significant difference was detected at a compression ratio of 20:1. A significant difference was observed with 40:1 compressed images for one reader (P = .023), and with 60:1 for all readers (P = .001 to .012). A root mean squared error (RMSE) was higher in 0.94- x 0.94-mm pixel size images than in 0.94- x 0.47-mm pixel size images at any compression ratio, indicating compression tolerance is lower for the larger pixel size images. The RMSE, subjective image quality, and error images of 10:1 compressed 0.94- x 0.94-mm pixel size images were comparable with those of 20:1 compressed 0.94- x 0.47-mm pixel size images. Wavelet compression can be acceptable clinically at ratios as high as 20:1 for brain MR images when a pixel size at image acquisition is around 1.0 x 0.5 mm, and as high as 10:1 for those with a pixel size around 1.0 x 1.0 mm.

Algorithms↗
PubMed2005

Physiological effects of indomethacin and celecobix: an S-transform laser Doppler flowmetry signal analysis.

Conventional signal processing typically involves frequency selective techniques which are highly inadequate for nonstationary signals. In this paper, we present an approach to perform time-frequency selective processing of laser Doppler flowmetry (LDF) signals using the S-transform. The approach is motivated by the excellent localization, in both time and frequency, afforded by the wavelet basis functions. Suitably chosen Gaussian wavelet functions are used to characterize the subspace of signals that have a given localized time-frequency support, thus enabling a time-frequency partitioning of signals. In this paper, the goal is to study the influence of various pharmacological substances taken by the oral way (celecobix (Celebrex), indomethacin (Indocid) and placebo) on the physiological activity behaviour. The results show that no statistical differences are observed in the energy computed from the time-frequency representation of LDF signals, for the myogenic, neurogenic and endothelial related metabolic activities between Celebrex and placebo, and Indocid and placebo. The work therefore proves that these drugs do not affect these physiological activities. For future physiological studies, there will therefore be no need to exclude patients having taken cyclo-oxygenase 1 inhibitions.

Administration, Oral↗
PubMed1994

Wavelet compression and segmentation of digital mammograms.

An initial evaluation of Haar wavelets is presented in this study for the compression of mammographic images. Fifteen mammograms with 105 microns/pixel resolution and varying dynamic range (10 and 12 bits per pixel) containing clustered microcalcifications were compressed with two different rates. The quality and content of the compressed reconstructed images was evaluated by an expert mammographer. The visualization of the cluster was on the average good but degraded with increasing compression because of the discontinuities introduced by these types of wavelets as the compression rate increases. However, the artifacts in the decoded images were seen as totally artificial and were not misinterpreted by the radiologist as calcifications. The classification of the parenchymal densities did not change significantly but the morphology of the calcifications was increasingly distorted as the compression rate increased leading to lower estimates of the suspiciousness of the cluster and higher uncertainties in the diagnosis. The uncompressed and two sets of compressed images were also processed by a wavelet method to extract the calcifications. Despite the fact that the segmentation algorithm generated several false-positive signals in highly compressed images, all true clusters were successfully segmented indicating that the compression process preserved the features of interest. Our preliminary results indicated that wavelets could be used to achieve high compression rates of mammographic images without losing small details such as microcalcification clusters as well as detect the calcifications from either the uncompressed or compressed reconstructed data. Further research and application of multiresolution analysis to digital mammography is continuing.

Algorithms↗
PubMed2000

Fixed-scale wavelet-type approximation of periodic density distributions.

For a chosen unit cell, a function defined in real space (a standard signal) is considered as a crystallographic wavelet-type function if it is localized in a small region of the real space, if its Fourier transform is likewise localized in reciprocal space, and if it is a periodical function which possesses a symmetry. The fixed-scale analysis consists in the decomposition of a studied distribution into a sum of copies of the same standard signal, but shifted into nodes of a grid in the unit cell. For a specified standard signal and grid of the permitted shifts in the unit cell, the following questions are discussed: whether an arbitrary function may be represented as the sum of the shifted standard signals; how the coefficients in the decomposition are calculated; what is the best fixed-scale approximation in the case that the exact decomposition does not exist. The interrelations between the fixed-scale decomposition and the phase problem, automatic map interpretation and density-modification methods are pointed out.

Journal Article↗
PubMed2000

A comparative study of transforms for use in digital audio data compression.

This paper provides a subjective quality analysis of transforms used in audio compression algorithms for a class of music signals. A 34-subject listener test compares three transforms in conjunction with an MPEG I layer 1 compression scheme. One test compares the performances of the discrete wavelet packet transform (DWPT) and the modified discrete cosine transform (MDCT) used in MPEG. Another test compares the performances of a DWPT eight-level nonuniform critical-band split and a DWPT five-level uniform subband split. Results indicate that the critical-band split provides significantly better quality than the uniform subband split for sounds with tonal and strong low-frequency content, while the DWPT outperforms the MDCT with significant improvement for nontonal sounds.

Algorithms↗
PubMed2006

The influence of acute progressive hypoxia on bioelectrical activity of the brain.

Hypoxia, a noxious and hyperventilatory stimulus and a modifier of neuronal metabolism, could influence cortical function. In this study we attempted to assess any such influence, its determinants, and particularly the role in it of the accompanying hypoxic emotional distress. We addressed the issue by examining the associations among EEG, ventilation, and anxiety during progressive poikilocapnic hypoxia (end-point SaO(2) approximately 75%) in 12 awake healthy volunteers (mean age 27.5 +/-0.7 yr). All subjects hyperventilated in response to hypoxia and 3 of them had a high level of anticipatory anxiety that forced one person to discontinue the test. We failed to show any major effect of hypoxia on the EEG pattern analyzed by visual inspection or wavelet power spectra. Therefore, no relationship between the ventilatory and cortical activity responses to hypoxia could be established. Cortical activity changes appeared, however, in the subjects who experienced emotional distress during the test. These changes were apparent on an expanded analysis of the EEG signal by the use of the Lempel-Ziv complexity that takes into account the ordering of variations in the signal, rather than only the relative frequency of events analyzed by the Shannon entropy. The Lempel-Ziv complexity offers promise as a novel method for unraveling fine and otherwise unexpressed alterations in cortical bioelectrical activity.

Adult↗
PubMed2002

[Statistical study of the wavelet-based lossy medical image compression technique].

Medical digital images have informational redundancy. Both the amount of memory for image storage and their transmission time could be reduced if image compression techniques are applied. The techniques are divided into two groups: lossless (compression ratio does not exceed 3 times) and lossy ones. Compression ratio of lossy techniques depends on visibility of distortions. It is a variable parameter and it can exceed 20 times. A compression study was performed to evaluate the compression schemes, which were based on the wavelet transform. The goal was to develop a set of recommendations for an acceptable compression ratio for different medical image modalities: ultrasound cardiac images and X-ray angiographic images. The acceptable image quality after compression was evaluated by physicians. Statistical analysis of the evaluation results was used to form a set of recommendations.

Angiography↗
PubMed2002

Frequency analysis of ventricular fibrillation in Swine ventricles.

It has been suggested from frequency analysis that cardiac fibrillation is driven by stable intramural reentry, with wavebreak occurring due to failure of 1:1 propagation. We tested this hypothesis with a combined experimental and theoretical approach. Optical mapping was performed on epicardial, endocardial, and transmural cut surfaces of fibrillating swine ventricles. Wavelets were characterized, the frequency content of optical signals analyzed, and space-time plots (STPs) constructed to detect Wenckebach-like conduction. The findings were compared with simulations in 2D and 3D cardiac tissue using the Luo-Rudy action potential model. The incidence of reentry in the cut transmural surface (11.8% in right ventricle, 14.3% in left ventricle) was similar to that on the endocardial surface (13.1%, P=NS) but greater than on the epicardial surface (7.7%, P<0.01). Frequency spectra of optically recorded membrane voltage were organized into spatial domains with the same dominant frequency, but these domains were nonstationary. In STPs, pseudo-2:1 conduction block was caused by double potentials arising when reentry occurred on the recording site rather than true Wenckebach conduction. The latter was observed in 11 of 166 STPs but did not occur at borders of high-to-low frequency domains. In simulations, similar findings were obtained when action potential duration (APD) restitution slope was steep. Stationary dominant frequency domains with Wenckebach conduction patterns were observed only in the presence of shallow APD restitution slope and marked nonuniform tissue heterogeneity. In conclusion, stable intramural reentry as the engine of fibrillation was not observed. Our findings support the idea that dynamic wavebreak plays a fundamental role in the generation and maintenance of ventricular fibrillation.

Action Potentials↗
PubMed1994

A pattern-analysis methodology for esophageal motility records.

The features of the esophageal contractile activity recorded during swallowing have been investigated using a signal-processing approach. Data of a 10 min recording taken from 10 normal subjects have been examined. The final features of each peristaltic wave are a set of parameters comprising the locations and magnitudes of the local wavelets contributing to the peristaltic. The approach is based on an inverse filtering technique. The inverse filter, designed with a knowledge of an average peristaltic profile formed by coherent averaging of many peristaltic examples aligned to their maxima, is used to refine the process of locating the local wavelets. It is shown that the inferred wavelet structure offers a good basis for modelling the different peristaltics, and that it suggests new parameters for esophageal characterization and classification procedures.

Deglutition↗
PubMed2005

Background correction in near-infrared spectra of plant extracts by orthogonal signal correction.

In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.

Algorithms↗