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PubMedDate not supplied

Implementation of wavelet-encoded MR imaging.

Reconstructions of images from wavelet-encoded data are shown. The method of MR wavelet encoding in one dimension was proposed previously by Weaver and Healy. The technique relies on selective excitation with wavelet-shaped profiles generated by special radio-frequency waveforms. The result of the imaging sequence is a set of inner products of the image with orthogonal functions of the wavelet basis. Inversion of the wavelet data is accomplished with an efficient algorithm with processing times comparable with those of a fast Fourier transform. The experiments show that wavelet encoding by selective excitation of wavelet-shaped profiles is feasible. Wavelet-encoded images are compared with phase-encoded images that have a similar signal-to-noise ratio, and there is no discernible degradation in image quality due to the wavelet encoding. Potential benefits of wavelet encoding are briefly discussed.

Artifacts↗
PubMed2006

A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.

This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.

Action Potentials↗
PubMed2004

[Methodological study on digitalization of tongue image in traditional Chinese medical diagnosis].

This is a research aimed at proposing a computerized tongue analysis method based on computerized image processing for quantizing the tongue properties in traditional Chinese medical diagnosis. The chromatic algorithm and 2-D Gabor wavelet transformation are applied to segmenting tongue from original image. The statistical method is adopted in identifying the colors of each pixel, which are attributed to the tongue substance and coating respectively. Thickness of tongue coating is determined by energy of 2-D Gabor wavelet coefficients (GWTE). The distribution of GWTE and invariant moment algorithm are used to judge the tongue texture. The experiment result shows that all methods proposed in this paper are effective.

Algorithms↗
PubMed2003

Design and analysis of a content-based pathology image retrieval system.

A prototype, content-based image retrieval system has been built employing a client/server architecture to access supercomputing power from the physician's desktop. The system retrieves images and their associated annotations from a networked microscopic pathology image database based on content similarity to user supplied query images. Similarity is evaluated based on four image feature types: color histogram, image texture, Fourier coefficients, and wavelet coefficients, using the vector dot product as a distance metric. Current retrieval accuracy varies across pathological categories depending on the number of available training samples and the effectiveness of the feature set. The distance measure of the search algorithm was validated by agglomerative cluster analysis in light of the medical domain knowledge. Results show a correlation between pathological significance and the image document distance value generated by the computer algorithm. This correlation agrees with observed visual similarity. This validation method has an advantage over traditional statistical evaluation methods when sample size is small and where domain knowledge is important. A multi-dimensional scaling analysis shows a low dimensionality nature of the embedded space for the current test set.

Abstracting and Indexing↗
PubMed2004

A new hybrid strategy for constructing a robust calibration model for near-infrared spectral analysis.

A new hybrid algorithm is proposed for construction of a high-quality calibration model for near-infrared (NIR) spectra that is robust against both spectral interference (including background and noise) and multiple outliers. The algorithm is a combination of continuous wavelet transform (CWT) and a modified iterative reweighted PLS (mIRPLS) procedure. In the proposed algorithm the spectral interference is filtered by CWT at the first stage then mIRPLS is proposed to detect the multiple outliers in the CWT domain. Compared with the original IRPLS method, mIRPLS does not need to adjust variable parameters to achieve optimum calibration results, which makes it very convenient to perform in practice. The final PLS model is constructed robustly because both the spectral interference and multiple outliers are eliminated. In order to validate the effectiveness and universality of the algorithm, it was applied to two different sets of NIR spectra. The results indicate that the proposed strategy can greatly enhance the robustness and predictive ability of NIR spectral analysis.

Journal Article↗
PubMed2004

A preliminary report on the role of spatial frequency analysis in the perception of breast cancers missed at mammography screening.

RATIONALE AND OBJECTIVES: Because several factors are involved in cancer detection, a malignant lesion that is visible on a mammogram will not necessarily be reported by the radiologist reading the case. Indeed, a significant fraction of screening-detected cancers are visible in retrospect, and were perceived by the radiologist when the case was read, but were either reported as benign findings or dismissed as variations of normal breast tissue. In this preliminary report the spatial frequency characteristics of clinically missed lesions are investigated by analyzing the mammogram acquired when the lesion was sent for biopsy and the most recent prior mammogram. For control purposes, the contralateral breast is also analyzed, when this breast is lesion free. MATERIALS AND METHODS: A database of 70 mammogram cases was assembled. Each case contained eight films: craniocaudal (CC) and mediolateral oblique (MLO) of the breast where a biopsy-proven lesion was found, CC and MLO of the contralateral breast, and CC and MLO of both breasts in the most recent prior mammogram. The dictated reports for all of these cases were obtained. Both benign and malignant lesions were used. The films were digitized and an region of interest surrounding each lesion was segmented from the image for processing using wavelet packets to extract spatial frequency information. The corresponding area was also segmented from the prior mammogram and from the contralateral breast, when this breast was lesion-free. Analysis of variance was used to determine if statistically significant differences existed between the derived features of cancer in the current and prior mammograms. RESULTS: The data suggests that malignant lesions reported in the prior mammogram as being benign differed from correctly reported malignant lesions and from correctly reported benign lesions. They also differed from nonreported malignant lesions. In addition, the spatial frequency representation of cancer significantly differed in the current and prior cases from the representation of normal breast tissue. CONCLUSION: Spatial frequency analysis may be useful to differentiate malignant lesions that are reported as benign and correctly reported benign lesions.

Algorithms↗
PubMed2002

Protein sequence comparison based on the wavelet transform approach.

A protein's chemical properties, the chain conformation, the function of the protein and its species specificity are determined by the information contained in the amino acid sequence. Proteins of similar functions have at some level sequential identical amino acid sequences. The closer the phylogenetic relationship, the more similar are the sequences. To find the similarities between two or more protein sequences is of great importance for protein sequence analysis. The differences in the amino acid sequences permit the construction of a family tree of evolution. In this work, a comparison method was devised that is capable of analysing a protein sequence 'hierarchically', i.e. it can examine a protein sequence at different spatial resolutions. Based on a wavelet decomposition of protein sequences and a cross-correlation study, a sequence-scale similarity concept is proposed for generating a similarity vector, which renders the comparison of two sequences feasible at different spatial resolutions (scales). This new similarity concept is an expansion of the conventional sequence similarity, which only takes into account the local pairwise amino acid match and ignores the information contained in coarser spatial resolutions.

Amino Acid Sequence↗
PubMed2005

Time-frequency analysis of transient neuromuscular events: dynamic changes in activity of the subthalamic nucleus and forearm muscles related to the intermittent resting tremor.

In order to investigate the dynamic change in transient neuromuscular events and the functional correlation between the neural and muscular activity, local field potentials (LFPs) of the subthalamic nucleus (STN) and surface electromyograms (sEMGs) over several episodes of transient resting tremor from a patient with Parkinson's disease were quantitatively characterised in time-frequency domain using short-time Fourier transform and continuous wavelet transform. Events of onset and cessation of the tremor-related activity in the STN and muscles were correlated to reveal the temporal relationship between the two signals. A significant suppression in the power of the STN LFPs in the beta band (10-30 Hz) preceded the onset of resting tremor, which was presented as the increases in the power at the tremor frequency (3.0-4.5 Hz) in both STN LFPs and surface EMGs. Over the episodes of the intermittent resting tremor, the power of the STN LFPs in the beta band and the power of sEMGs in the tremor frequency band change in an alternating pattern with a significant exponential correlation (P(STN) = 16.8+62.3 x exp(-P(EMG)/6270.7); R2 = 0.72; p < 0.05). Significant linear correlation in the power values at the tremor frequency appears between STN LFPs and sEMGs (P(STN) = 65.1 + 2.1 x 10(-4)P(EMG); R2 = 0.41; p < 0.05). In comparison with short-time Fourier transform, similar results could be achieved using continuous wavelet transform of an appropriate wavelet with a higher temporal resolution but larger distortion in the high frequency.

Electromyography↗
PubMed2004

Ipsilateral-mammogram computer-aided detection of breast cancer.

In this paper, an ipsilateral multi-view computer-aided detection (CAD) scheme is presented for mass detection in digital mammograms by exploiting correlative information of suspicious lesions between mammograms of the same breast. After nonlinear tree-structured filtering for image noise suppression, two wavelet-based methods, directional wavelet transform and tree-structured wavelet transform for image enhancement, and adaptive fuzzy C-means algorithm for segmentation are employed on each mammograms of the same breast, respectively, concurrent analysis is developed for iterative analysis of ipsilateral multi-view mammograms by inter-projective feature matching analysis. A supervised artificial neural network is developed as a classifier, in which the back-propagation algorithm combined with Kalman filtering is used as training algorithm, and free-response receiver operating characteristic analysis is used to test the performance of the developed unilateral CAD system. Performance comparison has been conducted between the final ipsilateral multi-view CAD system and our previously developed single-mammogram-based CAD system. The study results demonstrate the advantages of ipsilateral multi-view CAD method combined with concurrent analysis over current single-view CAD system on false positive reduction.

Breast Neoplasms↗
PubMed2004

Statistical characterization of real-world illumination.

Although studies of vision and graphics often assume simple illumination models, real-world illumination is highly complex, with reflected light incident on a surface from almost every direction. One can capture the illumination from every direction at one point photographically using a spherical illumination map. This work illustrates, through analysis of photographically acquired, high dynamic range illumination maps, that real-world illumination possesses a high degree of statistical regularity. The marginal and joint wavelet coefficient distributions and harmonic spectra of illumination maps resemble those documented in the natural image statistics literature. However, illumination maps differ from typical photographs in that illumination maps are statistically nonstationary and may contain localized light sources that dominate their power spectra. Our work provides a foundation for statistical models of real-world illumination, thereby facilitating the understanding of human material perception, the design of robust computer vision systems, and the rendering of realistic computer graphics imagery.

Contrast Sensitivity↗
PubMed2000

A digital density equalization technique to improve visualization of breast periphery in mammography.

In mammographic imaging, the film area corresponding to the breast periphery is overexposed, resulting in high optical density and degraded contrast in this region. A digital, model-driven density equalization technique was designed and developed to overcome this overexposure problem, taking into account the non-linear characteristic curve of the film-digitizer system. The method is based on several image processing and analysis techniques, such as thresholding, which is used to segment the pixels of the mammogram belonging to the breast region from the background, and wavelet-based fusion, which is used to equalize the pixels of breast periphery selectively while leaving the remaining breast region unaffected. Initial application of the method resulted in density-equalized mammographic images, characterized by improved contrast at the breast periphery.

Breast Neoplasms↗
PubMed1997

The performance and reliability of wavelet denoising for Doppler ultrasound fetal heart rate signal preprocessing.

The present paper deals with the performance and the reliability of a Wavelet Denoising method for Doppler ultrasound Fetal Heart Rate (FHR) recordings. It displays strong evidence that the denoising process extracts the actual noise components. The analysis is approached with three methods. First, the power spectrum of the denoised FHR displays more clearly an 1/fa scaling law, i.e. the characteristic of fractal time series. Second, the rescaled scale analysis technique reveals a Hurst exponent at the range of 0.7-0.8 that corresponds to a long memory persistent process. Moreover, the variance of the Hurst exponent across time scales is smaller at the denoised signal. Third, a chaotic attractor reconstructed with the embedding dimension technique becomes evident at the denoised signals, while it is completely obscured at the unfiltered ones.

Artifacts↗
PubMed2006

Multiscale hybrid linear models for lossy image representation.

In this paper, we introduce a simple and efficient representation for natural images. We view an image (in either the spatial domain or the wavelet domain) as a collection of vectors in a high-dimensional space. We then fit a piece-wise linear model (i.e., a union of affine subspaces) to the vectors at each downsampling scale. We call this a multiscale hybrid linear model for the image. The model can be effectively estimated via a new algebraic method known as generalized principal component analysis (GPCA). The hybrid and hierarchical structure of this model allows us to effectively extract and exploit multimodal correlations among the imagery data at different scales. It conceptually and computationally remedies limitations of many existing image representation methods that are based on either a fixed linear transformation (e.g., DCT, wavelets), or an adaptive uni-modal linear transformation (e.g., PCA), or a multimodal model that uses only cluster means (e.g., VQ). We will justify both quantitatively and experimentally why and how such a simple multiscale hybrid model is able to reduce simultaneously the model complexity and computational cost. Despite a small overhead of the model, our careful and extensive experimental results show that this new model gives more compact representations for a wide variety of natural images under a wide range of signal-to-noise ratios than many existing methods, including wavelets. We also briefly address how the same (hybrid linear) modeling paradigm can be extended to be potentially useful for other applications, such as image segmentation.

Algorithms↗
PubMedDate not supplied

Time delay estimation using wavelet transform for pulsed-wave ultrasound.

The windowed cross-correlation (WCC) technique has recently attracted attention in pulsed-wave (PW) ultrasound for measurement of tissue motion and blood flow velocity because of its performance advantages over the conventional Doppler method. The WCC measures tissue motion and blood flow velocity via estimation of time delays of backscattered signals in two consecutive echoes. In this paper, we propose a wavelet transform-based cross-correlation (WTCC) technique for the time delay estimation in PW ultrasound. The WTCC consists of three steps: (i) computing wavelet transforms (WTs) of received echoes, (ii) computing cross-correlations in the wavelet domain, and (iii) estimating the time delays by maximizing the estimated cross-correlations. Dyadic or continuous wavelets may be used in the proposed approach. The WTCC has a unique feature of using varying time-frequency windows in processing compared with the WCC which only uses a single fixed window. Our computer simulations show that, compared with the WCC, the WTCC provides a better estimation of time delays (lower failure rate and lower estimate error) and its performance is more consistent under various conditions, and more robust with window size. In the simulations, we also tested a specific continuous wavelet for the WTCC that was the emitted pulse itself and found the corresponding WTCC outperforms the WTCC with a regular dyadic wavelet.

Bias↗
PubMed2000

[Wavelet packet-based ECG compression].

A wavelet packet-based algorithm for the compression of ECG is presented in the paper. The wavclet packet algorithm, which synthesizes the efficiency and the flexibility of the wavelet pacheket, generates significantly lower data rates with less computational effort.

Algorithms↗
PubMed2005

A steerable complex wavelet construction and its application to image denoising.

This work addresses the design of a novel complex steerable wavelet construction, the generation of transform-space feature measurements associated with corner and edge presence and orientation properties, and the application of these measurements directly to image denoising. The decomposition uses pairs of bandpass filters that display symmetry and antisymmetry about a steerable axis of orientation. While the angular characterization of the bandpass filters is similar to those previously described, the radial characteristic is new, as is the manner of constructing the interpolation functions for steering. The complex filters have been engineered into a multirate system, providing a synthesis and analysis subband filtering system with good reconstruction properties. Although the performance of our proposed denoising strategy is currently below that of recently reported state-of-the-art techniques in denoising, it does compare favorably with wavelet coring approaches employing global thresholds and with an "Oracle" shrinkage technique, and presents a very promising avenue for exploring structure-based denoising in the wavelet domain.

Algorithms↗
PubMed2001

Time-Frequency analysis of heart rate variability during transient segments.

The spectral characteristics of heart rate variability (HRV) are related to the modulation of the autonomic nervous system. As the physiological condition is changed by such external stimuli such as drugs, postural changes, and anesthesia, or by internal deregulation such as in syncope, adjective autonomic responses could alter HRV characteristics. Time-frequency analysis is commonly used to investigate the time-related HRV characteristics. An alteration of the autonomic regulation resulting in a change in mean heart rate induces a transient component in heart rate, which, with any analysis method based on signals from multiple beats, results in the apparent spread of the spectrum of frequencies. This obscures the spectral components related to the autonomic function. In this paper we investigated the influence of the transient component in several time-frequency methods including the short-time Fourier transform, the Choi-Williams distribution, the smoothed pseudo Wigner-Ville distribution (SPWVD), the filtering SPWVD compensation, and the discrete wavelet transform. One simulated signal and two heart rate signals during general anesthesia and postural change were used for this assessment. The result demonstrates that the filtering SPWVD compensation and the discrete wavelet transform have small spectrum interference from the transient component.

Anesthesia, General↗
PubMed1997

Wave-intensity analysis: a new approach to left ventricular filling dynamics.

In order to explore a new approach to the analysis of diastolic dysfunction, we adapted wave-intensity analysis (WIA), a time-domain analysis that provides information regarding both upstream and downstream events, to left ventricular (LV) filling. WIA considers the pressure and flow waves as summations of successive wavelets, characterised by the direction they travel and by the sign of the pressure gradient associated with them. Wave intensity is the product, dPdU, calculated from the incremental differences in LV pressure (dP) and mitral velocity (dU) and, during the diastolic filling interval, yields up to five dPdU peaks. Peak 1 is caused by backward-travelling expansion waves that accelerate the blood while LV pressure falls, and may be related to "diastolic suction". Peak 2 is caused by forward-travelling compression waves which occur if acceleration continues after LV pressure begins to increase. Peak 3 is caused by backward compression waves and is associated with rising LV pressure and deceleration. Peak 4 is caused by forward compression waves and is associated with the increasing LV pressure and acceleration caused by atrial contraction. Peak 5 is caused by backward compression waves and is associated with increasing pressure and deceleration. These preliminary observations suggest that WIA can be useful in describing the mechanics of LV filling and, after much further work has been accomplished, it might prove useful in the detection and characterization of diastolic dysfunction.

Aorta↗