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PubMed1996

Investigating the effect of maternal alcohol intake on human fetal breathing rate using adaptive time-frequency analysis methods.

In this study, the matching pursuit (MP) method which is a modified version of the wavelet transform (WT) method was proposed to examine the effects of alcohol on human fetal breathing rates in both time and frequency domains. The matching pursuit method was chosen since the classical Fourier transform may not represent signals which have stationary characteristics and wavelet transform may not represent signals whose Fourier transforms have a narrow frequency support. Our results show that the horizontal structured atoms representing the sinusoidal activity at all frequency ranges disappeared and the vertical structured atoms representing the discontinuous spike type activity increased. In addition, the circular structured atoms at the high frequency range shifted to the low frequencies after the alcohol intake. The results also suggested that the matching pursuit is most suitable for analyzing the fetal breathing rate signals with and without alcohol intake.

Algorithms↗
PubMed2005

Detection of clustered microcalcifications in small field digital mammography.

The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps. First, we filter the original picture with a filter that is sensitive to microcalcification contrast shape. Then, we enhance the mammogram contrast by using wavelet-based sharpening algorithm. Afterwards, we present to radiologist, for visual analysis, such a contrast-enhanced mammogram with suggested positions of microcalcification clusters. We have evaluated the usefulness of the system with the help of four experienced radiologists, who found that it significantly improves the detection of microcalcifications in small field digital mammography.

Algorithms↗
PubMedDate not supplied

Wavelet based compression of medical ultrasound images using vector quantization.

In this paper, an efficient technique for compression of medical ultrasound (US) images is proposed. The technique is based on wavelet transform of the original image combined with vector quantization (VQ) of high-energy subbands using the LBG algorithm. First, we analyse the statistical behaviour of wavelet coefficients in US images across various subbands and scales. The analysis show that most of the image energy is concentrated in one of the detail subband, either in the vertical detail subband (most of the time) or in the horizontal subband. The other two subbands at each decomposition level contribute negligibly to the total image energy. Then, by exploiting this statistical analysis, a low-complexity image coder is designed, which applies VQ only to the highest energy subband while discarding the other detail subbands at each level of decomposition. The coder is tested on a series of abdominal and uterus greyscale US images. The experimental results indicate that the proposed method clearly outperforms the JPEG2000 (Joint Photographers Expert Group) encoder both qualitatively and quantitatively. For example, without using any entropy coder, the proposed method yields a peak signal to noise ratio gain of 0.2 dB to 1.2 dB over JPEG2000 on medical US images.

Algorithms↗
PubMed2003

Hierarchical active shape models, using the wavelet transform.

Active shape models (ASMs) are often limited by the inability of relatively few eigenvectors to capture the full range of biological shape variability. This paper presents a method that overcomes this limitation, by using a hierarchical formulation of active shape models, using the wavelet transform. The statistical properties of the wavelet transform of a deformable contour are analyzed via principal component analysis, and used as priors in the contour's deformation. Some of these priors reflect relatively global shape characteristics of the object boundaries, whereas, some of them capture local and high-frequency shape characteristics and, thus, serve as local smoothness constraints. This formulation achieves two objectives. First, it is robust when only a limited number of training samples is available. Second, by using local statistics as smoothness constraints, it eliminates the need for adopting ad hoc physical models, such as elasticity or other smoothness models, which do not necessarily reflect true biological variability. Examples on magnetic resonance images of the corpus callosum and hand contours demonstrate that good and fully automated segmentations can be achieved, even with as few as five training samples.

Algorithms↗
PubMed1998

Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms.

We are developing a computer-aided diagnosis (CAD) scheme for detection of clustered microcalcifications in digital mammograms. The use of an empirically chosen wavelet and scale combination for detection of microcalcifications as an initial step of the CAD scheme has been reported by us previously. In this study, we developed a technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of our CAD scheme based on the supervised learning method. In the learning process, an error function was formulated to represent the difference between a desired output and the reconstructed image obtained from weighted wavelet coefficients for a given mammogram. The error function was then minimized by modifying the weights for wavelet coefficients by means of a conjugate gradient algorithm. The Least Asymmetric Daubechies' wavelets were optimized with 297 regions of interest (ROIs) as a training set by a jackknife method. The performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis by use of the above set of ROIs. The analysis yielded an average area under the ROC curve of 0.92, which outperforms the difference-image technique used in our existing CAD scheme, as well as the partial reconstruction method used in our previous study.

Biophysical Phenomena↗
PubMed2006

A method for dynamic subtraction MR imaging of the liver.

BACKGROUND: Subtraction of Dynamic Contrast-Enhanced 3D Magnetic Resonance (DCE-MR) volumes can result in images that depict and accurately characterize a variety of liver lesions. However, the diagnostic utility of subtraction images depends on the extent of co-registration between non-enhanced and enhanced volumes. Movement of liver structures during acquisition must be corrected prior to subtraction. Currently available methods are computer intensive. We report a new method for the dynamic subtraction of MR liver images that does not require excessive computer time. METHODS: Nineteen consecutive patients (median age 45 years; range 37-67) were evaluated by VIBE T1-weighted sequences (TR 5.2 ms, TE 2.6 ms, flip angle 20 degrees , slice thickness 1.5 mm) acquired before and 45s after contrast injection. Acquisition parameters were optimized for best portal system enhancement. Pre and post-contrast liver volumes were realigned using our 3D registration method which combines: (a) rigid 3D translation using maximization of normalized mutual information (NMI), and (b) fast 2D non-rigid registration which employs a complex discrete wavelet transform algorithm to maximize pixel phase correlation and perform multiresolution analysis. Registration performance was assessed quantitatively by NMI. RESULTS: The new registration procedure was able to realign liver structures in all 19 patients. NMI increased by about 8% after rigid registration (native vs. rigid registration 0.073 +/- 0.031 vs. 0.078 +/- 0.031, n.s., paired t-test) and by a further 23% (0.096 +/- 0.035 vs. 0.078 +/- 0.031, p < 0.001, paired t-test) after non-rigid realignment. The overall average NMI increase was 31%. CONCLUSION: This new method for realigning dynamic contrast-enhanced 3D MR volumes of liver leads to subtraction images that enhance diagnostic possibilities for liver lesions.

Journal Article↗
PubMed2000

[Experiments on elimination of the noise in NEMG by adopting wavelet transform].

Wavelet transform (WT) is a promising technique for time-frequency analysis. By decomposing signals into elementary building blocks that are well localized both in time and frequency, the WT can characterize the local regularity of signal. In this paper, we process the needle electrode electromyography (NEMG) signal with noise by adopting WT and have completed the contrast experiments. The results show that WT is an effective method by which high frequency noise and baseline drift in NEMG can successfully be eliminated by selecting proper transform scales of WT.

Electromyography↗
PubMed2006

[QRS complexes detection based on Mexican-hat wavelet].

In this paper, we using Mexican-hat wavelet transform to detect characteristic points of ECG signal based on the characteristic points corresponding with the extremes of Mexican-hat wavelet transform. It offers a new detection method of ECG signal analysis. This method is simple and it is proved to be accurate and reliable. The correct rate of QRS detection rate examined by the MIT-BIT arrhythmia database rises up to 99.9%.

Algorithms↗
PubMed2006

Description of the transfer function of an optical system with wavelet transforms.

According to the wavefront filtering idea of wavelet optics, the transfer function of an optical system is described with a wavelet scale function. In the transfer function described with a wavelet scale function, different scale parameters a,c and shift parameters b,d correspond to different subtransfer functions, which correspond to different situations of the optical system. According to the request of the optical system, by adjusting all these scale parameters, not only can we obtain the optical images under different conditions, but we can also obtain the singular points under this scale parameter; hence a more ideal output can be obtained by such processing. The transfer function described with a wavelet scale function can be adjusted according to the request of the optical system, which makes the described transfer function self-adjustable. According to all types of disturbing effects to the system, by adjusting the scale and shift parameters, the practical form of the transfer function of an optical system can be confirmed, which satisfies the request of the self-adjustability of the optical imaging system. The result of our analysis shows that describing the transfer function of an optical system with a wavelet scale function is not only feasible but also satisfies the request of the self-adjustability of the optical imaging system, and different optical systems can be described by different wavelet scale parameters. This work breaks from the formal additional describing mode of the transfer function of an optical system and makes description of the transfer function of an optical system convenient.

Journal Article↗
PubMed2002

[ECG in the diagnosis of supraventricular tachyarrhythmias].

The development of catheter ablation techniques during the last decade provided new data about the mechanism of supraventricular tachyarrhythmias and at the same time, set new requirements for their classification. An accurate diagnosis of individual SVT can usually be made during an electrophysiologic study that precedes catheter ablation. Nevertheless, clinically acceptable differential diagnosis of SVT can be based on analysis of a standard 12-lead electrocardiogram. This may prove useful especially when selecting optimum antiarrhythmic drug according to a suspected mechanism of arrhythmia. At the same time, electrocardiogram during SVT serves as a recording of clinical arrhythmia for catheter ablation. At present, SVTs are divided into 3 main categories: 1. atrial tachyarrhythmias confined solely to atrial tissue, 2. tachycardias involving the AV junction, and 3. AV reentrant tachycardias involving one or more accessory connections with an electric impulse travelling between atria and ventricles. The first category can be further subdivided into: a) macroreentrant atrial tachycardias related to the presence of macroscopic anatomical or functional barriers; b) focal atrial tachycardias arising from a focus of abnormal automaticity or microreentry in the atrium; c) the syndrome of inappropriate sinus tachycardia resulting most probably from hypersensitivity to adrenergic stimulation; d) atrial fibrillation based on the existence of multiple wandering wavelets in the atria. Electrocardiographic differential diagnosis is predominantly based on an analysis of the standard 12-lead ECG. Principal diagnostic features include the presence and timing of the P waves in relation to the QRS complex. Additional criteria comprise the presence or absence of AV block during the tachycardia, an axis orientation of the P waves and their morphology, the appearance of QRS alternans or frequency of tachycardia.

Catheter Ablation↗
PubMed2002

Analysis of an adaptive strain estimation technique in elastography.

Elastography is based on the estimation of strain due to tissue compression or expansion. Conventional elastography involves computing strain as the gradient of the displacement (time-delay) estimates between gated pre- and postcompression signals. Uniform temporal stretching of the postcompression signals has been used to reduce the echo-signal decorrelation noise. However, a uniform stretch of the entire postcompression signal is not optimal in the presence of strain contrast in the tissue and could result in loss of contrast in the elastogram. This has prompted the use of local adaptive stretching techniques. Several adaptive strain estimation techniques using wavelets, local stretching and iterative strain estimation have been proposed. Yet, a quantitative analysis of the improvement in quality of the strain estimates overconventional strain estimation techniques has not been reported. We propose a two-stage adaptive strain estimation technique and perform a quantitative comparison with the conventional strain estimation techniques in elastography. In this technique, initial displacement and strain estimates using global stretching are computed, filtered and then used to locally shift and stretch the postcompression signal. This is followed by a correlation of the shifted and stretched postcompression signal with the precompression signal to estimate the local displacements and hence the local strains. As proof of principle, this adaptive stretching technique was tested using simulated and experimental data.

Elasticity↗
PubMed2004

Analysis and detection of binaural interaction in auditory evoked brainstem responses by time-scale representations.

The beta-wave of the binaural interaction component (BIC) in auditory evoked brainstem responses has been shown to be an objective measure of binaural interaction. However, a reliable and automated detection of this component capable of clinical use still remains a challenge. In this study, wavelet based time-scale representations of auditory evoked brainstem responses were investigated for the analysis of binaural interaction and for an automated detection of the beta-wave. Twenty normal hearing subjects with verified normal directional hearing and speech intelligibility in noise were included in our study. In all of these subjects, the BICs exhibited a characteristic concentration of energy in the time-scale domain which allowed for an automated detection of the beta-wave. Moreover, our study provides an explanation why the beta-wave is hard to detect for larger interaural time delays using time-scale entropy based arguments. It is concluded that time-scale representations of auditory brainstem responses are well suited for the analysis of binaural interaction and allow for an automated detection of the beta-wave.

Acoustic Stimulation↗
PubMed2005

The Allan factor: a new model of mathematical interpretation of heart rate variability in stable coronary artery disease. Preliminary results.

INTRODUCTION: The current analysis of heart rate variability (HRV) is one of the noninvasive methods of cardiovascular system assessment. The quantitative characteristics of the RR interval sequence and its dynamics are still under development as regards measurement techniques and development of new HRV interpretation models. The practical clinical application of the standard measurements is still insufficient and improvement of sensitivity and specificity of HRV parameters is needed. AIM: To assess a novel mathematical model of HRV interpretation and compare it with standard HRV measurements for patients with stable coronary artery disease (CAD), based on the virtual instrumentation technique. METHODS: The study group consisted of 24 patients with CAD confirmed by coronary angiography and a control group of 15 volunteers. Short-term electrocardiographic signals were recorded by a computer system and analysed for estimation of several HRV descriptors in time, frequency and combined time-frequency domains. Calculations included standard HRV measures and the Allan factor, a parameter based on the Haar wavelet transform. RESULTS: None of the investigated measurements derived from power spectral analysis has shown a statistically significant difference between healthy controls and patients with CAD, with the exception of rMSSD (Wilcoxon test: supine position *p=0.0018, erect position p=0.0708; discriminant function analysis: supine position *p=0.0069, erect position p=0.7851). Compared with standard HRV variables, the Allan factor better discriminated patients with CAD from healthy subjects (Wilcoxon test: supine position *p=0.0172, erect position *p=0.0001; discriminant function analysis: supine position p=0.8962, erect position *p=0.0200). CONCLUSIONS: The observations related to the novel parameter based on combined time and frequency domains may provide better quantitative measurements of heart rate variability in patients with coronary artery disease and require further investigations to assess their sensitivity and specificity.

Adult↗
PubMed2004

Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing.

We present a general wavelet-based denoising scheme for functional magnetic resonance imaging (fMRI) data and compare it to Gaussian smoothing, the traditional denoising method used in fMRI analysis. One-dimensional WaveLab thresholding routines were adapted to two-dimensional (2-D) images, and applied to 2-D wavelet coefficients. To test the effect of these methods on the signal-to-noise ratio (SNR), we compared the SNR of 2-D fMRI images before and after denoising, using both Gaussian smoothing and wavelet-based methods. We simulated a fMRI series with a time signal in an active spot, and tested the methods on noisy copies of it. The denoising methods were evaluated in two ways: by the average temporal SNR inside the original activated spot, and by the shape of the spot detected by thresholding the temporal SNR maps. Denoising methods that introduce much smoothness are better suited for low SNRs, but for images of reasonable quality they are not preferable, because they introduce heavy deformations. Wavelet-based denoising methods that introduce less smoothing preserve the sharpness of the images and retain the original shapes of active regions. We also performed statistical parametric mapping on the denoised simulated time series, as well as on a real fMRI data set. False discovery rate control was used to correct for multiple comparisons. The results show that the methods that produce smooth images introduce more false positives. The less smoothing wavelet-based methods, although generating more false negatives, produce a smaller total number of errors than Gaussian smoothing or wavelet-based methods with a large smoothing effect.

Algorithms↗
PubMed1998

Wavelet versus JPEG (Joint Photographic Expert Group) and fractal compression. Impact on the detection of low-contrast details in computed radiographs.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate different lossy image compression algorithms in direct comparison. METHODS: Computed radiographs were reviewed after compression with Wavelet, Fractal, and Joint Photographic Expert Group (JPEG) algorithms. For receiver operating characteristic (ROC) analysis, 54 thoracic computed radiographs (31 showing pulmonary nodules) were compressed with a ratio of 1:60. Five images of a test-phantom were coded at 1:13. All images were reviewed on a PC. Uncompressed images were reviewed at a PC and at a radiologic workstation (with image processing). RESULTS: For thorax images, decrease of diagnostic accuracy was significant with Wavelets. Fractal performed worse than Wavelets. No ROC curve was observed for JPEG due to poor image quality. No diagnostic loss was noted comparing PC and Workstation review. For low-contrast details of the phantom, results of Wavelet compression were equal to uncompressed images. Fewer true positives and increased true negatives were noted with Wavelets though. Wavelets were superior to JPEG, and JPEG images were superior to Fractal. Workstation review was superior to PC review. CONCLUSIONS: Only Wavelets provided accurate review of low-contrast details at a compression of 1:13. Frequency filtering of Wavelets affects contrast even at a low compression ratio. JPEG performed better than Fractal at low and worse at high compression ratio.

Algorithms↗
PubMed2005

Multi-spectral image analysis and classification of melanoma using fuzzy membership based partitions.

The sensitivity and specificity of melanoma diagnosis can be improved by adding the lesion depth and structure information obtained from the multi-spectral, trans-illumination images to the surface characteristic information obtained from the epi-illumination images. Wavelet transform based bi-modal channel energy features obtained from the images are used in the analysis. Methods using both crisp and fuzzy membership based partitioning of the feature space are evaluated. For this purpose, the ADWAT classification method that uses crisp partitioning is extended to handle multi-spectral image data. Also, multi-dimensional fuzzy membership functions with Gaussian and Bell profiles are proposed for classification. Results show that the fuzzy membership functions with Bell profile are more effective than the extended ADWAT method in discriminating melanoma from dysplastic nevus.

Diagnosis, Differential↗
PubMed2006

Over-complete discrete wavelet transformation of the normal auditory brainstem response improves prediction of outcome following severe acute closed head injury.

Previous research has shown that complex statistical analysis (discriminant function analysis) of a 'normal' auditory brainstem response (ABR) result can improve this measure's ability to predict subject outcome following severe acute closed head injury (ACHI). We hypothesized that adding the ABR's time-frequency information to such an analysis would improve this predictive value even further. 'Normal' ABR results were sampled from 69 severe ACHI subjects (22 of whom died and 47 of whom lived) and their time-frequency information extracted using an over-complete discrete wavelet transformation (OCDWT). A series of logistic regression analyses then showed correct predictions of death and survival as follows: ABR measures only 72 and 89% (respectively), ABR OCDWT measures only 82 and 89% (respectively), and ABR and ABR OCDWT measures combined 86 and 93% (respectively). These results showed that the addition of time-frequency information can improve the ability of the 'normal' ABR result to predict outcome following severe ACHI.

Acoustic Stimulation↗
PubMed2006

Toward quantitative short-echo-time in vivo proton MR spectroscopy without water suppression.

A methodological development for quantitative short-echo-time (TE) in vivo proton MR spectroscopy (MRS) without water suppression (WS) is described that integrates experimental and software approaches. Experimental approaches were used to eliminate frequency modulation sidebands and first-order phase errors. The dominant water signal was modeled and extracted by the matrix pencil method (MPM) and was used as an internal reference for absolute metabolite quantification. Spectral fitting was performed by combining the baseline characterization by a wavelet transform (WT)-based technique and time-domain (TD) parametric spectral analysis using full prior knowledge of the metabolite model spectra. The model spectra were obtained by spectral simulation instead of in vitro measurements. The performance of the methodology was evaluated by Monte Carlo (MC) studies, phantom measurements, and in vivo measurements on rat brains. More than 10 metabolites were quantified from spectra measured at TE = 20 ms on a 4.7 T system.

Algorithms↗