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PubMed2003

The perception of breast cancers--a spatial frequency analysis of what differentiates missed from reported cancers.

The primary detector of breast cancer is the human eye. Radiologists read mammograms by mapping exogenous and endogenous factors, which are based on the image and observer, respectively, into observer-based decisions. These decisions rely on an internal schema that contains a representation of possible malignant and benign findings. Thus, to understand the hits and misses made by the radiologists, it is important to model the interactions between the measurable image-based elements contained in the mammogram and the decisions made. The image-based elements can be of two types, i.e., areas that attracted the visual attention of the radiologist, but did not yield a report, and areas where the radiologist indicated the presence of an abnormal finding. In this way, overt and covert decisions are made when reading a mammogram. In order to model this decision-making process, we use a system that is based upon the processing done by the human visual system, which decomposes the areas under scrutiny in elements of different sizes and orientations. In our system, this decomposition is done using wavelet packets (WPs). Nonlinear features are then extracted from the WP coefficients, and an artificial neural network is trained to recognize the patterns of decisions made by each radiologist. Afterwards, the system is used to predict how the radiologist will respond to visually selected areas in new mammogram cases.

Artificial Intelligence↗
PubMed2004

Computer-aided diagnosis of mammographic microcalcification clusters.

Computer-aided diagnosis techniques in medical imaging are developed for the automated differentiation between benign and malignant lesions and go beyond computer-aided detection by providing cancer likelihood for a detected lesion given image and/or patient characteristics. The goal of this study was the development and evaluation of a computer-aided detection and diagnosis algorithm for mammographic calcification clusters. The emphasis was on the diagnostic component, although the algorithm included automated detection, segmentation, and classification steps based on wavelet filters and artificial neural networks. Classification features were selected primarily from descriptors of the morphology of the individual calcifications and the distribution of the cluster. Thirteen such descriptors were selected and, combined with patient's age, were given as inputs to the network. The features were ranked and evaluated for the classification of 100 high-resolution, digitized mammograms containing biopsy-proven, benign and malignant calcification clusters. The classification performance of the algorithm reached a 100% sensitivity for a specificity of 85% (receiver operating characteristic area index Az = 0.98 +/- 0.01). Tests of the algorithm under various conditions showed that the selected features were robust morphological and distributional descriptors, relatively insensitive to segmentation and detection errors such as false positive signals. The algorithm could exceed the performance of a similar visual analysis system that was used as basis for development and, combined with a simple image standardization process, could be applied to images from different imaging systems and film digitizers with similar sensitivity and specificity rates.

Algorithms↗
PubMed2002

Noise reveals visual mechanisms of detection and discrimination.

When performance is limited by stochastically defined masks, (psychophysical) reverse correlation has proven to be an especially efficient tool for estimating the templates used by detection and discrimination mechanisms. Here I describe a maximum-likelihood approach to quantifying the significance of differences between estimates of template. Four methodologically related experiments illustrate the versatility of reverse correlation. Experiment 1 shows significant differences between the templates used by different observers when detecting a bright Gaussian blob. The results of Experiment 2 are consistent with observers not using information about the phase of a parafoveal wavelet when detecting it. Experiments 3 and 4 reveal not only the templates used by detection mechanisms but also aspects of their response functions. Both results are consistent with a sensory threshold. Experiment 3 shows that 2-alternative forced-choice detection errors are caused when the target's effective contrast is reduced, not when the mask looks more like the expected target+mask than the actual target+mask. Experiment 4 suggests that observers use optimally tuned detection templates for orientation discrimination.

Choice Behavior↗
PubMed2004

A grid computing infrastructure for MEG data analysis.

Magnetoencephalography (MEG) is widely used for studying brain functions, but clinical applications of MEG have been less prevalent. One reason is that only clinicians who have highly specialized knowledge can use MEG diagnostically, and such clinicians are found at only a few major hospitals. Another reason is that MEG data analysis is getting more and more complicated, and deals with a large amount of data, and thus requires high-performance computing. These problems can be solved by the collaboration of human and computing resources distributed in multiple facilities. A new computing infrastructure for brain scientists and clinicians in distant locations was therefore developed by the Grid technology, which provides virtual computing environments composed of geographically distributed computers and experimental devices. A prototype system connecting an MEG system at the AIST in Japan, a Grid environment composed of PC clusters at Osaka University in Japan and Nanyang Technological University in Singapore, and user terminals in Baltimore was developed. MEG data measured at the AIST were transferred in real-time through a 1-GB/s network to the PC clusters for processing by a wavelet cross-correlation method, and then monitored in Baltimore. The current system is the basic model for remote-access to MEG equipment and high-speed processing of MEG data.

Computing Methodologies↗
PubMed2003

Signal denoising and baseline correction by discrete wavelet transform for microchip capillary electrophoresis.

Signal denoising and baseline correction using discrete wavelet transform (DWT) are described for microchip capillary electrophoresis (MCE). DWT was performed on an electropherogram describing a separation of nine tetramethylrohodamine-5-isothiocyanate labeled amino acids, following MCE with laser-induced fluorescence detection, using Daubechies 5 wavelet at a decomposition level of 6. The denoising efficiency was compared with, and proved to be superior to, other commonly used denoising techniques such as Fourier transform, Savitzky-Golay smoothing and moving average, in terms of noise removal and peak preservation by directly visual inspection. Novel strategies for baseline correction were proposed, with a special interest in baseline drift that frequently occurred in chromatographic and electrophoretic separations.

Algorithms↗
PubMed2006

Artifact reduction for simultaneous EEG/fMRI recording: adaptive FIR reduction of imaging artifacts.

OBJECTIVE: We present a new method of effectively removing imaging artifacts of electroencephalography (EEG) and extensively conserving the time-frequency features of EEG signals during simultaneous functional magnetic resonance imaging (fMRI) scanning under conventional conditions. METHODS: Under the conventional conditions of a 5000 Hz EEG sampling rate, but in the absence of the MRI slice-timing signals, the imaging artifact during each slice scanning is theoretically inferred to be a linear combination of the average artifact waveform and its derivatives, deduced by band-limited Taylor's expansion. Technically, the imaging artifact reduction algorithm is equivalent to an adaptive finite impulse response (FIR) filter. RESULTS: The capability of this novel method removing the imaging artifacts of EEG recording during fMRI scanning has been demonstrated by a phantom experiment. Moreover, the effectiveness of this method in conserving the time-frequency features of EEG activity has been evaluated by both visually evoked experiments and alpha waves. CONCLUSIONS: The adaptive FIR method is an effective method of removing the imaging artifacts under conventional conditions, and also conserving the time-frequency EEG signals. SIGNIFICANCE: The proposed adaptive FIR method, removing the imaging artifacts, combined with the wavelet-based non-linear noise reduction (WNNR) method [Wan X, Iwata K, Riera J, Ozaki T, Kitamura M, Kawashima R. Artifact reduction for EEG/fMRI recording: Nonlinear reduction of ballistocardiogram artifacts. Clin Neurophysiol 2006;117:668-80], reducing the ballistocardiogram artifacts (BAs), makes it feasible to obtain accurate EEG signals from the simultaneous EEG recordings during fMRI scanning.

Adult↗
PubMed1999

Synthesis and decomposition of transient-evoked otoacoustic emissions based on an active auditory model.

In this paper, transient-evoked otoacoustic emissions (TEOAE's) are synthesized by an active auditory model, and decomposed by continuous wavelet transform (CWT) to study the frequency-latency relationship and the generation of TEOAE signals. The controlled voltage sources that relate to the mobile mechanism of the outer hair cells (OHC's) are proposed to serve as the generation sources of TEOAE signals. The state-variable method is adopted to calculate the auditory model. The mother wavelet used in CWT is selected on basis of the model. The results of this study show that the simulated TEOAE signal is similar to the clinically detected ones not only in the time-domain waveform, but also in the frequency-latency relationship. It seems to be clear that the generation of TEOAE signals is related to the same active mechanism as the cochlear sharp frequency selectivity.

Adult↗
PubMed2004

Beat-to-Beat ECG ventricular late potentials variance detection by filter bank and wavelet transform as beat-sequence filter.

This paper presents a novel method that employs a wavelet transform and filter bank to detect ventricular late potentials (VLPs) from beat to beat in order to keep its variance. Conventionally, three time-domain features, which are highly related to the QRS complex endpoint, are generally accepted as criteria for classifying VLPs. Signal averaging is a general and effective de-noising method in electroencephalogram late potentials detection, but it may also eliminate the beat-to-beat variance. Other types of filter applied to the time sequence may destroy the late potentials as well when trying to filter out the noise. To preserve the variance from beat to beat as well as late potentials as much as possible, the concept of a beat-sequence filter will be introduced and the wavelet transform can be directly applied to the beat sequence, as will be demonstrated in this paper. After de-noising, instead of applying the voltage comparison on the de-noised signal to determine the QRS complex endpoint, the signal will be processed by a filter bank, and the QRS complex endpoint will be determined by consideration of the correlation between two beats. Both simulation and clinical experimental results will be presented to illustrate the effectiveness of this method.

Action Potentials↗
PubMed2006

Adaptive MAP error concealment for dispersively packetized wavelet-coded images.

In this paper, we present an adaptive maximum a posteriori (MAP) error concealment algorithm for dispersively packetized wavelet-coded images. We model the subbands of a wavelet-coded image as Markov random fields, and use the edge characteristics in a particular subband, and regularity properties of subband/wavelet samples across scales, to adapt the potential functions locally. The resulting adaptive MAP estimation gives PSNR advantages of up to 0.7 dB compared to the competing algorithms. The advantage is most evident near the edges, which helps improve the visual quality of the reconstructed images.

Algorithms↗
PubMed1966

Intracellular autogenetic effects of muscular contration on extensor motoneurones. The silent period.

1. Intracellular records have been taken from cat motoneurones belonging to gastrocnemius and soleus or to popliteal synergists during contractions of gastrocnemius and soleus, acting separately or jointly. Such contractions were elicited by brief tetani or single shocks to the peripheral end of the cut ventral roots L7 or S1.2. Hyperpolarization of the motoneurone accompanies rise of tension in contraction. The amount of it increases when at constant extension the contraction of the muscle is increased by increasing stimulus strength, as well as when it is increased by augmenting extension at constant stimulus strength. It is therefore tension-sensitive.3. The duration of the hyperpolarization induced in this manner reflects the duration of the contraction itself, being considerably longer in the slow soleus than in the faster gastrocnemius. It is often preceded by a brief wavelet of depolarization ascribed to the so-called back-response.4. Early in relaxation there occurs a transient ;hump' of membrane depolarization. This corresponds to the moment characterized by phasic bursts from the spindle primaries. The ;hump' terminates hyperpolarization.5. When the cell is stimulated by injected current to maintained repetitive firing, the ;silent period' in contraction begins with the phase of hyperpolarization and ends with the hump of depolarization as described above.6. Later during relaxation, delayed inhibition, may or may not follow often accompanied by hyperpolarizing activation noise and sometimes also visible as an extension of the silent period of a firing cell. There is, however, no marked hyperpolarization of the motoneurone in delayed inhibition.7. In the Discussion the events described above are related to previous studies employing monosynaptic testing or electromyography for the analysis of the variations of excitability caused in extensor motoneurones by autogenetic contractions as well as to known properties of stretch receptors.

Animals↗
PubMed2007

Intraclass retrieval of nonrigid 3D objects: application to face recognition.

As the size of the available collections of 3D objects grows, database transactions become essential for their management with the key operation being retrieval (query). Large collections are also precategorized into classes so that a single class contains objects of the same type (e.g., human faces, cars, four-legged animals). It is shown that general object retrieval methods are inadequate for intraclass retrieval tasks. We advocate that such intraclass problems require a specialized method that can exploit the basic class characteristics in order to achieve higher accuracy. A novel 3D object retrieval method is presented which uses a parameterized annotated model of the shape of the class objects, incorporating its main characteristics. The annotated subdivision-based model is fitted onto objects of the class using a deformable model framework, converted to a geometry image and transformed into the wavelet domain. Object retrieval takes place in the wavelet domain. The method does not require user interaction, achieves high accuracy, is efficient for use with large databases, and is suitable for nonrigid object classes. We apply our method to the face recognition domain, one of the most challenging intraclass retrieval tasks. We used the Face Recognition Grand Challenge v2 database, yielding an average verification rate of 95.2 percent at 10-3 false accept rate. The latest results of our work can be found at http://www.cbl.uh.edu/UR8D/.

Algorithms↗
PubMed2005

Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding--Part II: application results.

The application of the wavelet transform-fractal dimension-based (WT-FD) filter of Part I of this paper to real bioacoustic data, which include explosive lung sounds (ELS) and explosive bowel sounds (EBS) recorded from patients with pulmonary or gastrointestinal dysfunction, respectively, is presented in this paper. The objective of the latter is the evaluation of the performance of the WT-FD filter on different types of bioacoustic signals, varying not only in their structural morphology but also in the degree of their noise contamination. As it is thoroughly described in Part I of this paper, the WT-FD filter uses the fractal dimension to form an efficient way of thresholding the WT coefficients at different resolution scales, keeping, thus, only those that can contribute to the accurate reconstruction of the ELS and EBS signals. Quantitative and qualitative analysis of the experimental results show an efficient performance of the WT-FD filter to circumvent the noise presence (100% detectability rate, 100% sensitivity, 100% specificity) by faithfully extracting the authentic structure of ELS and EBS from the background noise. The WT-FD filter does not require any noise reference signal or noise reference templates. The results from a noise stress test (mean cross-correlation index of the original and the estimated signal converging to 100%; mean normalized maximum amplitude error converging to 0.7%) prove its robustness to various noise levels (0-20 dB), enabling its potential use in similar noise cases met in everyday clinical medicine. Furthermore, the efficient performance of the WT-FD filter facilitates the physician to better interpret the auscultation findings. Due to its simplicity and low computational cost, the WT-FD filter can possibly be implemented in a real-time context to serve as a tool for the continuous ELS and EBS screening.

Adult↗
PubMed2004

Preprocessing of two-dimensional gel electrophoresis images.

Proteomics produces a huge amount of two-dimensional gel electrophoresis images. Their analysis can yield a lot of information concerning proteins responsible for different diseases or new unidentified proteins. However, an automatic analysis of such images requires an efficient tool for reducing noise in images. This allows proper detection of the spots' borders, which is important in protein quantification (as the spots' areas are used to determine the amounts of protein present in an analyzed mixture). Also in the feature-based matching methods the detected features (spots) can be described by additional attributes, such as area or shape. In our study, a comparison of different methods of noise reduction is performed in order to find out a method best suited for reducing noise in gel images. Among the compared methods there are the classical methods of linear filtering, e.g., the mean and Gaussian filtering, the nonlinear method, i.e., median filtering, and also the methods better suited for processing of nonstationary signals, such as spatially adaptive linear filtering and filtering in the wavelet domain. The best results are obtained by filtering of gel images in the wavelet domain, using the BayesThresh method of threshold value determination.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2004

Increased identification of peptides by enhanced data processing of high-resolution MALDI TOF/TOF mass spectra prior to database searching.

This paper presents application of sequential enhanced data processing procedures to high-resolution tandem mass spectra for identification of peptides using the Mascot database search algorithm. A strategy for (1) selection of fragment ion peaks from MS/MS spectra, (2) utilization of improved mass accuracy of the precursor ions, and (3) wavelet denoising of the mass spectra prior to fragment ion selection have been developed. The number of peptide identifications obtained using the enhanced processing was then compared with that obtained using software provided by the instrument manufacturer. Approximately 9000 MS/MS spectra acquired by the Applied Biosystems 4700 TOF/TOF MS instrument were used as a model data set. After application of the new processing, an increase of 33% unique peptides and 22% protein identifications with at least two unique peptides were found. The influence of the processing on the percentage of false positives, estimated by searching against a randomized database, was estimated to increase false positive identifications from 2.7 to 3.9%, which was still below the 5% error rate specified in the Mascot search. These data processing approaches increase the amount of information that can be extracted from LC-MS analysis without the necessity of additional experiments.

Algorithms↗
PubMed2006

3-band motion-compensated temporal structures for scalable video coding.

Recent breakthroughs in motion-compensated temporal wavelet filtering have finally enabled implementation of highly efficient scalable and error-resilient video codecs. These new wavelet codecs provide numerous advantages over nonscalable conventional solutions techniques based on motion-compensated prediction, such as no recursive predictive loop, separation of noise and sampling artifacts from the content through use of longer temporal filters, removal of long range as well as short range temporal redundancies, etc. Moreover, these wavelet video coding schemes can provide flexible spatial, temporal, signal-to-noise ratio and complexity scalability with fine granularity over a large range of bit rates, while maintaining a very high coding efficiency. However, most motion-compensated wavelet video schemes are based on classical two-band decompositions that offer only dyadic factors of temporal scalability. In this paper, we propose a three-band temporal structure that extends the concept of motion-compensated temporal filtering (MCTF) that was introduced in the classical lifting framework. These newly introduced structures provide higher temporal scalability flexibility, as well as improved compression performance compared with dyadic Haar MCTF.

Algorithms↗
PubMed1998

Monotonic noise suppression used to improve the sensitivity of fMRI activation maps.

We have introduced a new method of removing noise from images that identifies significant extrema and forces the pixel intensities between any two extrema to change monotonicly. The method has some similarities to wavelet denoising methods we worked with several years ago but is generally more stable and is effective on images with lower SNR's. In this paper the method of monotonic filtering is used to increase the sensitivity in functional magnetic resonance imaging (fMRI) studies. We have used the increased sensitivity to improve the temporal resolution in fMRI studies by roughly a factor of six. A motor activation study was acquired with single slice 256 x 256 pixel T2* weighted images; six cycles of finger tapping were acquired. Each cycle consisted of five images of rest followed by five images of right hand finger tapping followed by five images of left hand finger tapping. The z-scores were calculated and used as the activation map. The left and right activations were both clearly visible when all six cycles were used in the analysis. However, no definitive activation was seen for any one cycle. When the original 256 x 256 images were averaged down to 64 x 64 pixel images before calculation of the z-scores, the activations were partially identified. When the original images were filtered using the monotonic noise reduction algorithm, the left activation was clearly visible in three of the six cycles and partially visible in two others. The right activation was partially visible in 4 out of 6 cycles. Optimized noise reduction should improve the results significantly. The ability to use a single cycles is very important in fMRI studies because many stimuli are more difficult to maintain over many cycles and because complex processes such as in cognitive or memory activity do not have simple responses.

Brain Mapping↗
PubMed2005

A new fractional wavelet approach for the simultaneous determination of ampicillin sodium and sulbactam sodium in a binary mixture.

A new application of the fractional wavelet transform (FWT) was proposed for the simultaneous determination of ampicillin (AP) and sulbactam (SB) in a pharmaceutical combination for injection. FWT approach is a new powerful tool for removing noise and irrelevant information from the absorption spectra. Cardinal information having higher peak amplitude, eliminated noise, sharp peaks with shrinking width of spectral range was obtained by the application of FWT procedure to the original absorption spectra. In this paper, FWT approach was subjected to the data vector of the UV-signals obtained from AP and SB in the wavelength range of 211.5-313.8 nm. Derivative transform was applied to the original absorption signal together with its FWT generalization. The calibration graphs for AP and SB were obtained by measuring the FWT and usual derivative amplitudes at zero-crossing points. The method validation was carried out by using the synthetic mixture analysis. Our proposed FWT approach was compared with the usual derivative spectrophotometry and chemometric methods (CLS, PCR and PLS) and a good agreement was reported.

Absorption↗
PubMed2006

Asymptotically optimal blind estimation of multichannel images.

Optimal estimation of a two-dimensional (2-D) multichannel signal ideally decorrelates the data in both channel and space and weights the resulting coefficients according to their SNR. Many scenarios exist where the required second-order signal and noise statistics are not known in which the decorrelation is difficult or expensive to calculate. An asymptotically optimal estimation scheme proposed here uses a 2-D discrete wavelet transform to approximately decorrelate the signal in space and the discrete Fourier transform to decorrelate between channels. The coefficient weighting is replaced with a wavelet-domain thresholding operation to result in an efficient estimation scheme for both stationary and nonstationary signals. In contrast to optimal estimation, this new scheme does not require second-order signal statistics, making it well suited to many applications. In addition to providing vastly improved visual quality, the new estimator typically yields signal-to-noise ratio gains 12 dB or higher for hyperspectral imagery and functional magnetic resonance images.

Algorithms↗