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PubMed2005

Acquisition time reduction in magnetic resonance spectroscopic imaging using discrete wavelet encoding.

This paper describes a new magnetic resonance spectroscopic imaging (MRSI) technique based upon the discrete wavelet transform to reduce acquisition time and cross voxel contamination. Prototype functions called wavelets are used in wavelet encoding to localize defined regions in localized space by dilations and translations. Wavelet encoding in MRSI is achieved by matching the slice selective RF pulse profiles to a set of dilated and translated wavelets. Single and dual band slice selective excitation and refocusing pulses, with profiles resembling Haar wavelets, are used in a spin-echo sequence to acquire 2D-MRSI wavelet encoding data. The 2D space region is spanned up to the desired resolution by a proportional number of dilations (increases in the localization gradients) and translations (frequency shift) of the Haar wavelets (RF pulses). Acquisition time is reduced by acquiring successive MR signals from regions of space with variable size and different locations with no requirement for a TR waiting time between acquisitions. An inverse wavelet transform is performed on the data to produce the correct spatial MR signal distribution.

Algorithms↗
PubMed1997

Denoising of the fetal heart rate signal with non-linear filtering of the wavelet transform maxima.

The fetal heart rate (FHR) signal provides valuable information for fetal development and well-being. However, the FHR traces derived from present-day ultrasound cardiotocographs are not of the desired quality. The paper applies the wavelet transform (WT) in order to denoise effectively the FHR signal. The denoising procedure analyses the evolution of the WT maxima across scales. The singularities of the signal create wavelet maxima with different properties from those of the induced noise. Since it is difficult to formulate precise rules that distinguish between the wavelet maxima of the FHR signal from those of the noise we have trained a neural network for this classification task. The neural network draws out successfully the noise induced wavelet maxima. An improved FHR signal can be obtained from the coarser wavelet approximation signal component and the filtered wavelet maxima by means of the inverse dyadic wavelet transform. Also, feature extraction and processing algorithms can be defined on the denoised wavelet coefficients (instead of on the original signal).

Artifacts↗
PubMed2006

Measurement of soluble solids contents and pH in orange juice using chemometrics and vis-NIRS.

The potential of visible and near-infrared reflectance spectroscopy (vis-NIRS) was investigated for its ability to nondestructively detect soluble solids contents (SSC) and pH in orange juices. A total of 104 orange juice samples were used for vis-NIRS at 325-1075 nm using a field spectroradiometer. Wavelet packet transform, standard normal variate transformation (SNV), and Savitzky-Golay first-derivative transformation were applied for the preprocessing of spectral data. The chemometrics of partial least-squares (PLS) regression analysis was performed on the processed spectral data. The evaluation of SSC and pH in orange juices by PLS regression with SNV showed the highest accuracy of the three preprocessing methods. The correlation coefficient (r), standard error of prediction, and the root-mean-square error of prediction for SSC were 0.98, 0.68, and 0.73, respectively, whereas those values for pH were 0.96, 0.06, and 0.06, respectively. The "fingerprint" representing features of orange juices or reflecting sensitivity to some elements at a certain band was proposed on the basis of regression coefficients. It is very useful in the field of food chemistry and further research on other materials. It is concluded that the vis-NIRS technique combined with chemometrics is promising for the fast and nondestructive detection of chemical components in orange juices or other materials.

Beverages↗
PubMed2006

A multiresolution image based approach for correction of partial volume effects in emission tomography.

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the "à trous" algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in an epileptic patient.

Algorithms↗
PubMed2001

A multistage perceptual quality assessment for compressed digital angiogram images.

This paper describes a multistage perceptual quality assessment (MPQA) model for compressed images. The motivation for the development of a perceptual quality assessment is to measure (in)visible differences between original and processed images. The MPQA produces visible distortion maps and quantitative error measures informed by considerations of the human visual system (HVS). Original and decompressed images are decomposed into different spatial frequency bands and orientations modeling the human cortex. Contrast errors are calculated for each frequency and orientation, and masked as a function of contrast sensitivity and background uncertainty. Spatially masked contrast error measurements are then made across frequency bands and orientations to produce a single perceptual distortion visibility map (PDVM). A perceptual quality rating (PQR) is calculated from the PDVM and transformed into a one to five scale, PQR(1-5), for direct comparison with the mean opinion score, generally used in subjective ratings. The proposed MPQA model is based on existing perceptual quality assessment models, while it is differentiated by the inclusion of contrast masking as a function of background uncertainty. A pilot study of clinical experiments on wavelet-compressed digital angiogram has been performed on a sample set of angiogram images to identify diagnostically acceptable reconstruction. Our results show that the PQR(1-5) of diagnostically acceptable lossy image reconstructions have better agreement with cardiologists' responses than objective error measurement methods, such as peak signal-to-noise ratio A Perceptual thresholding and CSF-based Uniform quantization (PCU) method is also proposed using the vision models presented in this paper. The vision models are implemented in the thresholding and quantization stages of a compression algorithm and shown to produce improved compression ratio performance with less visible distortion than that of the embedded zerotrees wavelet (EZWs).

Algorithms↗
PubMed2005

An orthogonal family of quincunx wavelets with continuously adjustable order.

We present a new family of two-dimensional and three-dimensional orthogonal wavelets which uses quincunx sampling. The orthogonal refinement filters have a simple analytical expression in the Fourier domain as a function of the order lamda, which may be noninteger. We can also prove that they yield wavelet bases of L2(R2) for any lambda > 0. The wavelets are fractional in the sense that the approximation error at a given scale a decays like O(a(lamda)); they also essentially behave like fractional derivative operators. To make our construction practical, we propose a fast Fourier transform-based implementation that turns out to be surprisingly fast. In fact, our method is almost as efficient as the standard Mallat algorithm for separable wavelets.

Algorithms↗
PubMed1998

Fully automated health monitoring at home.

The aim of this study was to provide adequate home health monitoring for the elderly at home using fully automated signal measurement with personal identification to support daily health care and to improve quality of life. We attempted to develop a home health monitoring system that did not provide any restrictions during sleep, bathing and excretion. A new room including bath and toilet was constructed. The system consisted of monitoring devices and a computer terminal for collecting data. The data were automatically obtained from monitoring devices situated at the bed, bath and toilet and were transferred to a data terminal and stored for further analysis. Where subjects live with their spouse or family, personal identification is required to store data individually. For the bathtub data, personal identification was performed using ECG data. Sampled data were reduced by wavelet transform and distinguished by a neural network. The data acquisition system is now underway and data are being extensively collected.

Aged↗
PubMed2002

Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model.

We have developed a new computer-aided diagnosis scheme for automated detection of lung nodules in digital chest radiographs based on a combination of morphological features and the wavelet snake. In our scheme, two processes were applied in parallel to reduce the false-positive detections after initial nodule candidates were selected. One process consisted of adaptive filtering for enhancement of nodules and suppression of normal lung structures, followed by extraction of conventional morphological features. The other process consisted of a novel approach for elimination of false positives called the edge-guided wavelet snake model. In the latter process, multiscale edges of the candidate nodules were extracted to yield parts of the nodule boundaries. A wavelet snake was then used for fitting of these multiscale edges for approximation of the true boundaries of nodules. A boundary feature called the weighted overlap between the snake and the multiscale edges was calculated and used for elimination of false positives. Finally, the weighted overlap and the morphological features were combined by use of an artificial neural network for efficient reduction of false positives. Our scheme was applied to a publicly available database of digital chest images for pulmonary nodules. Receiver operating characteristic analysis was employed for evaluation of the performance of each process in the scheme. The combined features yielded a large reduction of false positives, and thus achieved a high performance in discriminating between true and false positives. These results show that our new method, in particular the false-positive reduction method based on the wavelet snake, is effective in improving the performance of a computerized scheme for detection of pulmonary nodules in chest radiographs.

Adult↗
PubMed2006

Assessment of the classification capability of prediction and approximation methods for HRV analysis.

The goal of this paper is to examine the classification capabilities of various prediction and approximation methods and suggest which are most likely to be suitable for the clinical setting. Various prediction and approximation methods are applied in order to detect and extract those which provide the better differentiation between control and patient data, as well as members of different age groups. The prediction methods are local linear prediction, local exponential prediction, the delay times method, autoregressive prediction and neural networks. Approximation is computed with local linear approximation, least squares approximation, neural networks and the wavelet transform. These methods are chosen since each has a different physical basis and thus extracts and uses time series information in a different way.

Adult↗
PubMed2004

Automatic identification of the pectoral muscle in mammograms.

The pectoral muscle represents a predominant density region in most medio-lateral oblique (MLO) views of mammograms; its inclusion can affect the results of intensity-based image processing methods or bias procedures in the detection of breast cancer. Local analysis of the pectoral muscle may be used to identify the presence of abnormal axillary lymph nodes, which may be the only manifestation of occult breast carcinoma. We propose a new method for the identification of the pectoral muscle in MLO mammograms based upon a multiresolution technique using Gabor wavelets. This new method overcomes the limitation of the straight-line representation considered in our initial investigation using the Hough transform. The method starts by convolving a group of Gabor filters, specially designed for enhancing the pectoral muscle edge, with the region of interest containing the pectoral muscle. After computing the magnitude and phase images using a vector-summation procedure, the magnitude value of each pixel is propagated in the direction of the phase. The resulting image is then used to detect the relevant edges. Finally, a post-processing stage is used to find the true pectoral muscle edge. The method was applied to 84 MLO mammograms from the Mini-MIAS (Mammographic Image Analysis Society, London, U.K.) database. Evaluation of the pectoral muscle edge detected in the mammograms was performed based upon the percentage of false-positive (FP) and false-negative (FN) pixels determined by comparison between the numbers of pixels enclosed in the regions delimited by the edges identified by a radiologist and by the proposed method. The average FP and FN rates were, respectively, 0.58% and 5.77%. Furthermore, the results of the Gabor-filter-based method indicated low Hausdorff distances with respect to the hand-drawn pectoral muscle edges, with the mean and standard deviation being 3.84 +/- 1.73 mm over 84 images.

Algorithms↗
PubMed2006

A vertical-energy-thresholding procedure for data reduction with multiple complex curves.

Due to the development of sensing and computer technology, measurements of many process variables are available in current manufacturing processes. It is very challenging, however, to process a large amount of information in a limited time in order to make decisions about the health of the processes and products. This paper develops a "preprocessing" procedure for multiple sets of complicated functional data in order to reduce the data size for supporting timely decision analyses. The data type studied has been used for fault detection, root-cause analysis, and quality improvement in such engineering applications as automobile and semiconductor manufacturing and nanomachining processes. The proposed vertical-energy-thresholding (VET) procedure balances the reconstruction error against data-reduction efficiency so that it is effective in capturing key patterns in the multiple data signals. The selected wavelet coefficients are treated as the "reduced-size" data in subsequent analyses for decision making. This enhances the ability of the existing statistical and machine-learning procedures to handle high-dimensional functional data. A few real-life examples demonstrate the effectiveness of our proposed procedure compared to several ad hoc techniques extended from single-curve-based data modeling and denoising procedures.

Algorithms↗
PubMed2006

Improvement of quantification of myocardial first-pass perfusion mapping: a temporal and spatial wavelet denoising method.

Mapping of myocardial blood flow (MBF) with first-pass perfusion imaging is becoming an important tool in the study of coronary artery disease. In this study a wavelet-based denoising method was developed to improve the accuracy of pixel-by-pixel MBF maps. We performed an in vivo study in five stenotic dogs with 70% stenosis in the left coronary arteries. First-pass perfusion imaging sessions were performed by administering the intravascular contrast agent Gadomer at rest and during dipyridamole-induced vasodilation. Color microspheres (MS) were injected into the dogs to measure MBF at the same time. After denoising was performed, the signal-to-noise ratio (SNR) of the first-pass perfusion image improved by approximately 180%, whereas spatial variation of MBF maps decreased 38%. It was also found that the correlation of MBFs measured by MRI with the MS method indicates a significant improvement with the denoising method (R2 increased from 0.24 to 0.78, P < .001). This suggests that the wavelet denoising method may be an effective way to increase the accuracy of pixel-by-pixel MBF quantification and reduce spatial variation, and may be applicable to other forms of noise-sensitive image analysis.

Animals↗
PubMed1983

A simple method for reliable separation of cerebellar Purkinje cell complex and simple spikes.

During the analysis of cerebellar Purkinje cell firing the use of two level discriminators for the separation of complex spike (CS) and simple spike (SS) can produce "wrong SS-events", since the amplitude of the CS wavelets may exceed the discrimination level for the SS. This is also the case, when the initial spike of the CS is negatively deflected. A logic circuit was developed, which ensures reliable separation of the two types of spike by a mutual control of the two channels. The CS wavelet events are obtained via an additional channel.

Animals↗
PubMed2002

Mapping of sp2/sp3 in DLC thin film by signal processed ESI series energy-loss image.

A set of signal processing methods comprising fast Fourier transform interpolation, maximum entropy deconvolution and wavelet transformation has been successfully integrated to improve the equality of the extracted C K-edge spectra from electron spectroscopic imaging (ESI) series. Fast Fourier transform interpolation is used to improve the dispersion arising from discrete sampling of ESI series in the energy space. The maximum entropy method is used to dispel the convolution effect resulting from that ESI series acquired with a finite energy window. Wavelet transformation is applied to de-noise the extracted ESI spectrum. The post-processed ESI spectrum has quality as good as that of a probe-acquired spectrum and makes semi-quantitative analysis of the two-dimensional sp2/sp3 ratio map in diamond-like carbon thin film possible. In general, this method is applicable for reconstructing good quality core-loss electron energy-loss spectra from a nanometre-sized area, so that it may be possible to quantitatively analyse two-dimensional information about electronic structure in materials with near nanometre resolution.

Journal Article↗
PubMed2003

Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly.

A new method of physical activity monitoring is presented, which is able to detect body postures (sitting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking," 8.2% for sitting, and 1.3% for lying.

Acceleration↗
PubMed2006

Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots.

We propose a method to detect and track multiple moving biological spot-like particles showing different kinds of dynamics in image sequences acquired through multidimensional fluorescence microscopy. It enables the extraction and analysis of information such as number, position, speed, movement, and diffusion phases of, e.g., endosomal particles. The method consists of several stages. After a detection stage performed by a three-dimensional (3-D) undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different biologically realistic movement types. Tracks are constructed, thereafter, by a data association algorithm based on the maximization of the likelihood of each IMM. The last stage consists of updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performances of the method are validated on synthetic image data and used to characterize the 3-D movement of endocytic vesicles containing quantum dots.

Algorithms↗
PubMed2005

Feature analysis of pathological speech signals using local discriminant bases technique.

Speech is an integral part of the human communication system. Various pathological conditions affect the vocal functions, inducing speech disorders. Acoustic parameters of speech are commonly used for the assessment of speech disorders and for monitoring the progress of the patient over the course of therapy. In the last two decades, signal-processing techniques have been successfully applied in screening speech disorders. In the paper, a novel approach is proposed to classify pathological speech signals using a local discriminant bases (LDB) algorithm and wavelet packet decompositions. The focus of the paper was to demonstrate the significance of identifying the signal subspaces that contribute to the discriminatory characteristics of normal and pathological speech signals in a computationally efficient way. Features were extracted from target subspaces for classification, and time-frequency decomposition was used to eliminate the need for segmentation of the speech signals. The technique was tested with a database of 212 speech signals (51 normal and 161 pathological) using the Daubechies wavelet (db4). Classification accuracies up to 96% were achieved for a two-group classification as normal and pathological speech signals, and 74% was achieved for a four-group classification as male normal, female normal, male pathological and female pathological signals.

Algorithms↗
PubMed2006

Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals.

Knee joint disorders are common in the elderly population, athletes, and outdoor sports enthusiasts. These disorders are often painful and incapacitating. Vibration signals [vibroarthrographic (VAG)] are emitted at the knee joint during the swinging movement of the knee. These VAG signals contain information that can be used to characterize certain pathological aspects of the knee joint. In this paper, we present a noninvasive method for screening knee joint disorders using the VAG signals. The proposed approach uses wavelet packet decompositions and a modified local discriminant bases algorithm to analyze the VAG signals and to identify the highly discriminatory basis functions. We demonstrate the effectiveness of using a combination of multiple dissimilarity measures to arrive at the optimal set of discriminatory basis functions, thereby maximizing the classification accuracy. A database of 89 VAG signals containing 51 normal and 38 abnormal samples were used in this study. The features extracted from the coefficients of the selected basis functions were analyzed and classified using a linear-discriminant-analysis-based classifier. A classification accuracy as high as 80% was achieved using this true nonstationary signal analysis approach.

Algorithms↗