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PubMed1999

Digital mammography: comparison of adaptive and nonadaptive CAD methods for mass detection.

RATIONALE AND OBJECTIVES: The authors compared the performance of adaptive and nonadaptive computer-aided diagnostic (CAD) methods for breast mass detection with digital mammography. MATERIALS AND METHODS: Both adaptive and nonadaptive modular CAD methods employed recent advances in multiresolution and mutiorientation wavelet transforms for improved feature extraction. The nonadaptive method uses fixed parameters for the image preprocessing modules. The adaptive method, a new class of algorithms, adapts to image content by selecting parameters for the image preprocessing modules within a parameter range. Comparison of the two methods was performed for each individual CAD module with a region-of-interest (ROI) database containing all mass types and normal tissue. RESULTS: Receiver operating characteristic (ROC) analysis clearly demonstrated an improvement in performance for the three adaptive modules and a significant overall difference between the two methods. The average ROC area index (Az) values were 0.86 and 0.95 for the nonadaptive and adaptive methods, respectively. The corresponding P value is .0145. For a previously reported database of full mammographic images containing 50 abnormal cases with all mass types and 50 normal images, the adaptive CAD method had a sensitivity of 96% (1.71 false-positive results per image) compared with 89% (1.91 false-positive results per image) for the nonadaptive CAD method. CONCLUSION: The adaptive CAD method demonstrated better performance. A study is in progress to determine the generalizability of the adaptive CAD method by applying it to larger retrospective image databases with different film digitizers.

Algorithms↗
PubMed2000

Dynamics of wavelets and their role in atrial fibrillation in the isolated sheep heart.

BACKGROUND: The multiple wavelet hypothesis is the most commonly accepted mechanism underlying atrial fibrillation (AF). However, high frequency periodic activity has recently been suggested to underlie atrial fibrillation in the isolated sheep heart. We hypothesized that in this model, multiple wavelets during AF are generated by fibrillatory conduction away from periodic sources and by themselves may not be essential for AF maintenance. METHODS AND RESULTS: We have used a new method of phase mapping that enables identification of phase singularities (PSs), which flank individual wavelets during sustained AF. The approach enabled characterization of the initiation, termination, and lifespan of wavelets formed as a result of wavebreaks, which are created by the interaction of wave fronts with functional and anatomical obstacles in their path. AF was induced in six Langendorff-perfused sheep hearts in the presence of acetylcholine. High resolution video imaging was utilized in the presence of a voltage sensitive dye; two-dimensional phase maps were constructed from optical recordings. The major results were as follows: (1) the critical inter-PS/wavelet distance for the formation of rotors was 4 mm, (2) the spatial distribution of wavelets/PSs was non-random. (3) the lifespan of PSs/wavelets was short; 98% of PSs/wavelets existed for < 1 rotation, and (4) the mean number of waves that entered our mapping field (15.7 +/- 1.6) exceeded the mean number of waves that exited it (9.7 +/- 1.5; P < 0.001). CONCLUSIONS: Our results strongly suggest that multiple wavelets may result from breakup of high frequency organized waves in the isolated Langendorff-perfused sheep heart, and as such are not a robust mechanism for the maintenance of AF in our model.

Acetylcholine↗
PubMed1999

[Wavelet application to reduction of stimulus artifact in transient evoked otoacoustic emissions testing].

Otoacoustic emissions(OAEs) are presently considered as an objective clinical test for assessing the integraity of peripheral hearing. Owing to the adding of the reflex waves of the stimuli, recordings of emissions evoked in response to transient stimuli are contaminated by an initial artifact, which inhibits the examination of high frequency cochlear responses that have short latencies. So OAEs testing is of serious limitation in most clinical environments. We propose an artifact reduction technique based on discrete wavelet transform pre-processing method and demonstrate empirically that the method not only improves artifact reduction but also enhances signal-to-noise ratio in the response region.

Acoustic Stimulation↗
PubMed2005

Application of two chemometric methods for the determination of imipramine, amitriptyline and perphenazine in content uniformity and drug dissolution studies.

Double-divisor spectra derivative and partial least squares methods were developed for content uniformity and dissolution tests in binary or ternary mixtures. The simultaneous determinations of perphenazine (PER) combined with amitriptyline hydrochloride (AMI) and/or imipramine hydrochloride (IMI) have been accomplished using the information of the absorption spectra of appropriate solutions. The double-divisor method is based on the use of the first derivative of the ratio spectrum obtained by dividing the absorption spectrum of the ternary mixture PER-AMI-IMI by a standard spectrum resulted from the addition of two of the three analytes in equal concentrations. The concentration of each component is then determined from their respective calibration graphs established by measuring the ratio derivative analytical signal at a specific wavelength. In this method, the linear determination ranges were of 3.65-18.24 microg/mL for PER, 4.32-21.60 microg/mL for AMI, and 4.83-24.19 microg/mL for IMI. The results were compared with those obtained by partial least squares multivariate calibration (PLS) method pre-treated by a wavelet compression-orthogonal signal correction (W-OSC) filter in zero-order derivative spectra. The calibration model was evaluated by internal validation (cross-validation) and by external validation over synthetic mixtures, content uniformity and dissolution tests. According to the dissolution profile test more than 95% of the three substances were dissolved within 10 min. The results from both techniques were statistically compared with each other and can be satisfactorily used for quantitative analysis and dissolution tests of multicomponent tablets.

Amitriptyline↗
PubMed2006

Translation-invariant contourlet transform and its application to image denoising.

Most subsampled filter banks lack the feature of translation invariance, which is an important characteristic in denoising applications. In this paper, we study and develop new methods to convert a general multichannel, multidimensional filter bank to a corresponding translation-invariant (TI) framework. In particular, we propose a generalized algorithme à trous, which is an extension of the algorithme à trous introduced for 1-D wavelet transforms. Using the proposed algorithm, as well as incorporating modified versions of directional filter banks, we construct the TI contourlet transform (TICT). To reduce the high redundancy and complexity of the TICT, we also introduce semi-translation-invariant contourlet transform (STICT). Then, we employ an adapted bivariate shrinkage scheme to the STICT to achieve an efficient image denoising approach. Our experimental results demonstrate the benefits and potential of the proposed denoising approach. Complexity analysis and efficient realization of the proposed TI schemes are also presented.

Algorithms↗
PubMed1998

Real-time separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter.

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS, by isolating them from VS, based on their nonstationarity, is presented in this paper. The proposed algorithm uses two adaptive network-based fuzzy inference systems to compose a generalized fuzzy rule-based stationary-nonstationary filter (GFST-NST). The training procedure of the fuzzy inference systems involves the outputs of the wavelet transform-based stationary-nonstationary filter (WTST-NST), proposed by Hadjileontiadis and Panas [1]. The basic idea of the GFST-NST was initially proposed by the authors with the introduction of the fuzzy rule-based stationary-nonstationary filter (FST-NST) [2], tested with the separation of crackles from VS. The main contribution of this paper is the modification of the structure of the FST-NST filter to a serial-type fuzzy filter that, unlike the parallel operation of the FST-NST filter, sends a predicted stationary signal (VS) into the predictor of the nonstationary (DAS). Applying the GFST-NST filter to fine-coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. The separation performance of the GFST-NST filter was evaluated through quantitative and qualitative indexes that proved its efficiency and superiority against the FST-NST filter. When compared to the WTST-NST filter, the GFST-NST filter performed similarly in accuracy and objectiveness, but in a faster way. Thus, the GFST-NST filter combines the separation accuracy of the WTST-NST filter with the real-time implementation of the FST-NST filter, so it can easily be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

Auscultation↗
PubMed2006

Improved Chou-Fasman method for protein secondary structure prediction.

BACKGROUND: Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades. The Chou-Fasman algorithm, one of the earliest methods, has been successfully applied to the prediction. However, this method has its limitations due to low accuracy, unreliable parameters, and over prediction. Thanks to the recent development in protein folding type-specific structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome. RESULTS: We improved Chou-Fasman method in three aspects. (a) Replace the nucleation regions with extreme values of coefficients calculated by the continuous wavelet transform. (b) Substitute the original secondary structure conformational parameters with folding type-specific secondary structure propensities. (c) Modify Chou-Fasman rules. The CB396 data set was tested by using improved Chou-Fasman method and three indices: Q3, Qpre, SOV were used to measure this method. We compared the indices with those obtained from the original Chou-Fasman method and other four popular methods. The results showed that our improved Chou-Fasman method performs better than the original one in all indices, about 10-18% improvement. It is also comparable to other currently popular methods considering all the indices. CONCLUSION: Our method has greatly improved Chou-Fasman method. It is able to predict protein secondary structure as good as current popular methods. By locating nucleation regions with refined wavelet transform technology and by calculating propensity factors with larger size data set, it is likely to get a better result.

Algorithms↗
PubMed2006

A robust method for heart sounds localization using lung sounds entropy.

Heart sounds are the main unavoidable interference in lung sound recording and analysis. Hence, several techniques have been developed to reduce or cancel heart sounds (HS) from lung sound records. The first step in most HS cancellation techniques is to detect the segments including HS. This paper proposes a novel method for HS localization using entropy of the lung sounds. We investigated both Shannon and Renyi entropies and the results of the method using Shannon entropy were superior. Another HS localization method based on multiresolution product of lung sounds wavelet coefficients adopted from was also implemented for comparison. The methods were tested on data from 6 healthy subjects recorded at low (7.5 ml/s/kg) and medium 115 ml/s/kg) flow rates. The error of entropy-based method using Shannon entropy was found to be 0.1 +/- 0.4% and 1.0 +/- 0.7% at low and medium flow rates, respectively, which is significantly lower than that of multiresolution product method and those of other methods reported in previous studies. The proposed method is fully automated and detects HS included segments in a completely unsupervised manner.

Adult↗
PubMed2006

Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals.

The quantification of phase synchrony between brain signals is of crucial importance for the study of large-scale interactions in the brain. Current methods are based on the estimation of the stability of the phase difference between pairs of signals over a time window, within successive frequency bands. This paper introduces a new approach to study the dynamics of brain synchronies, Frequency Flows Analysis (FFA). It allows direct tracking and characterization of the nonstationary time-frequency dynamics of phase synchrony among groups of signals. It is based on the use of the one-to-one relationship between frequency locking and phase synchrony, which applies when the concept of phase synchrony is not taken in an extended 'statistical' sense of a bias in the distribution of phase differences, but in the sense of a continuous phase difference conservation during a short period of time. In such a case, phase synchrony implies identical instantaneous frequencies among synchronized signals, with possible time varying frequencies of synchronization. In this framework, synchronous groups of signals or neural assemblies can be identified as belonging to common frequency flows, and the problem of studying synchronization becomes the problem of tracking frequency flows. We use the ridges of the analytic wavelet transforms of the signals of interest in order to estimate maps of instantaneous frequencies and reveal sustained periods of common instantaneous frequency among groups of signal. FFA is shown to track complex dynamics of synchrony in coupled oscillator models, reveal the time-frequency and spatial dynamics of synchrony convergence and divergence in epileptic seizures, and in MEG data the large-scale ongoing dynamics of synchrony correlated with conscious perception during binocular rivalry.

Adult↗
PubMed2003

Asymmetrical singularities in real-world signals.

We generalize the wavelet transform modulus maxima approach in order to analyze positive and negative changes separately and show different singularity spectra depending on the direction of changes in (i) human heartbeat interval data during sympathetic blockade, (ii) time series of daytime human physical activity of healthy individuals (but not of patients with debilitating fatigue), and (iii) daily stock price records of the Nikkei 225 in the period 1990-2002--but not of the S&P 500. We conclude that the analysis of asymmetrical singularities provides deeper insights into the underlying complexity of real-world signals that can greatly enhance our understanding of the mechanisms determining the systems' dynamics.

Adrenergic beta-Antagonists↗
PubMed2005

Multidimensional, mapping-based complex wavelet transforms.

Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3M - 1)/(2M - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processing applications, we have demonstrated state-of-the-art results.

Algorithms↗
PubMed1998

Assessment of intersignal variability for discrimination of atrial fibrillation from atrial flutter.

The analysis of endocardial signals obtained from an electrode located in the right atrium enabled by new dual chamber implantable cardioverter defibrillators may be helpful to provide additional therapies such as overdrive pacing or low energy atrial cardioversion for the treatment of concomitant atrial flutter (AFL) or atrial fibrillation (AF). Algorithms for discrimination of atrial tachyarrhythmias based on rate counting are of limited efficacy. The aim of this study was to assess the intersignal variability by using fast discrete wavelet transforms (FDWT) as a new method of discrimination of AF from AFL. Patients with spontaneous episodes of AF/AFL or patients who developed AF/AFL during an electrophysiological study were studied. The endocardial signals were recorded from the high right atrium using a transvenous 5 Fr bipolar electrode catheter (interelectrode spacing: 1 cm). The signals were digitized (2 kHz, 12-bit resolution) after amplification and filtering (40-500 Hz). Within data segments of 10-second duration, 25 consecutive signals were selected and normalized and FDWT was applied. Standard deviations of the wavelet coefficients (SD) from coarse scales (scale 4-8) were calculated. A total of 94 data segments (AF: 52, AFL: 42) from 28 patients were analyzed. SD at each considered scale was higher for AF than for AFL (P < 0.001). SD at scale 8 discriminated between AF from AFL with 100% sensitivity and specificity. We conclude that assessment of intersignal variability of bipolar endocardial recordings using FDWT is an effective method for the discrimination of AF from AFL. The implementation of this tool in a discrimination algorithm of an implantable device may help provide the appropriate differential therapy for atrial tachyarrhythmias.

Algorithms↗
PubMed2002

Multi-resolution and wavelet representations for identifying signatures of disease.

Identifying physiological and anatomical signatures of disease in signals and images is one of the fundamental challenges in biomedical engineering. The challenge is most apparent given that such signatures must be identified in spite of tremendous inter and intra-subject variability and noise. Crucial for uncovering these signatures has been the development of methods that exploit general statistical properties of natural signals. The signal processing and applied mathematics communities have developed, in recent years, signal representations which take advantage of Gabor-type and wavelet-type functions that localize signal energy in a joint time-frequency and/or space-frequency domain. These techniques can be expressed as multi-resolution transformations, of which perhaps the best known is the wavelet transform. In this paper we review wavelets, and other related multi-resolution transforms, within the context of identifying signatures for disease. These transforms construct a general representation of signals which can be used in detection, diagnosis and treatment monitoring. We present several examples where these transforms are applied to biomedical signal and imaging processing. These include computer-aided diagnosis in mammography, real-time mosaicking of ophthalmic slit-lamp imagery, characterization of heart disease via ultrasound, predicting epileptic seizures and signature analysis of the electroencephalogram, and reconstruction of positron emission tomography data.

Algorithms↗
PubMedDate not supplied

Application of keyhole imaging to interventional MRI: a simulation study to predict sequence requirements.

A wide variety of techniques have been proposed recently to improve the temporal resolution of MRI. These include echo-planar imaging methods, wavelet encoding, singular value decomposition encoding, and k-space sharing methods known as "keyhole" imaging. In this work, we use a simulation study to investigate the phase-encoding ordering and data-sharing methods required for the application of keyhole imaging to interventional MRI (I-MRI). The advantages of keyhole imaging over other methods are its simplicity and the use of conventional phase encoding and Fourier transform reconstruction found on virtually all modern MR imagers. Our analysis has predicted that conventional keyhole methods that repeatedly acquire only the center portion of k space, and those that sequentially progress from the center of k space outward, will not meet the combination of temporal and spatial resolution required for tip localization during I-MRI needle insertion. Instead, acquisitions that acquire both high and low k-space data, in ranked order, should provide acceptable tip position and needle width accuracy in both temporal and spatial domains for use in I-MRI.

Artifacts↗
PubMed2006

Resolution enhancement in digital x-ray imaging.

We have developed a restoration method for radiographs that enhances image sharpness and reveals bone microstructures that were initially hidden in the soft-tissue glare. The method is two fold: the image is first deconvolved using the Richardson-Lucy algorithm and is then divided with a signal modelling the soft-tissue distribution to increase the overall contrast. Each step has its own merits but the power of the restoration method lies in their combination. The originality of the method is its reliance on a priori information at each step in the processing. We have measured and modelled analytically the point-spread function of a low-dose gas microstrip x-ray detector at several beam energies. We measured the relationship between the local image intensity and the noise variance for these images. The soft-tissue signal was also modelled using a minimum-curvature filtering technique. These results were then combined into an image deconvolution procedure that uses wavelet filtering to reduce restoration noise while keeping the enhanced small-scale features. The method was applied successfully to images of a human-torso phantom and improved the contrast of small details on the bones and in the soft tissues. We measured a mean 54% increase in signal to noise ratio and a mean 105% increase in contrast to noise ratio in the 70 and 140 kVp images we analysed. The method was designed to facilitate the analysis of radiographs by relying on two levels of visual inspection. The contrast of the full image is first enhanced by division with the signal modelling the soft-tissue distribution. Based on the result, a radiologist might decide to zoom in on a given image section. The full restoration method is then applied to that region of interest. Indeed, full image deconvolution is often unnecessary since enhanced small-scale details are not visible at large scale; only the section of interest is processed which is more efficient.

Algorithms↗
PubMed2005

[A wavelet-transform-based method for the automatic detection of late-type stars].

The LAMOST project, the world largest sky survey project, urgently needs an automatic late-type stars detection system. However, to our knowledge, no effective methods for automatic late-type stars detection have been reported in the literature up to now. The present study work is intended to explore possible ways to deal with this issue. Here, by "late-type stars" we mean those stars with strong molecule absorption bands, including oxygen-rich M, L and T type stars and carbon-rich C stars. Based on experimental results, the authors find that after a wavelet transform with 5 scales on the late-type stars spectra, their frequency spectrum of the transformed coefficient on the 5th scale consistently manifests a unimodal distribution, and the energy of frequency spectrum is largely concentrated on a small neighborhood centered around the unique peak. However, for the spectra of other celestial bodies, the corresponding frequency spectrum is of multimodal and the energy of frequency spectrum is dispersible. Based on such a finding, the authors presented a wavelet-transform-based automatic late-type stars detection method. The proposed method is shown by extensive experiments to be practical and of good robustness.

Algorithms↗
PubMedDate not supplied

[Calculation of local Hurst exponents in the Ca(2+)-activated K(+)-channel dwell time].

A novel method based on the maximum overlap wavelet transform of dwell time series is proposed. Information on local multifractal properties of the series, namely local Hurst exponents or Holder exponents, was obtained. The results confirm the presence of multifractality and intrinsic correlations in the Ca(2+)-activated K+ channel dwell time series. The data on the local multifractal structure of the series can be interpreted in terms of processes having self-organized criticality. The proposed approach allows one to widen the store of methods for the analysis of single ion channel activity.

Algorithms↗