Computationally Efficient Cosine Modulated Filter Bank Design for ECG Signal Compression

In this work, computationally efficient and reliable cosine modulated filter banks (CMFBs) are designed for Electrocardiogram (ECG) data compression. First of all, CMFBs (uniform and non-uniform) are designed using interpolated finite impulse response (IFIR) prototype filter to reduce the computational complexity. To reduce the reconstruction error, linear iteration technique is applied to optimize the prototype filter. Then after, non-uniform CMFB is used for ECG data compression by decomposing ECG signal into various frequency bands. Subsequently, thresholding is applied for truncating the insignificant coefficients. The estimation of the threshold value is done by examining the significant energy of each band. Further, Run-length encoding (RLE) is utilized for improving the compression performance. The method is applied to MIT-BIH arrhythmia database for performance analysis of the proposed work. The experimental observations demonstrate that the proposed method has accomplished high compression ratio with the admirable quality of signal reconstruction. The proposed work provides the average values of compression ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), quality score (QS), correlation coefficient (CC), maximum error (ME), mean square error (MSE), and signal to noise ratio (SNR) are 23.86, 1.405, 2.55, 19.08, 0.999, 0.12, 0.054 and 37.611 dB, respectively. The proposed 8-channel uniform filter bank is used to detect the R-peak locations of the ECG signal. The comparative analysis shows that beats (locations and amplitudes) of both signals (original and reconstructed signals) are same.     

Removal of ECG interference from surface respiratory electromyography

The signal to noise ratio (SNR) of surface respiratory electromyography signal is very low. Indeed EMG signal is contaminated by different types of noise especially the cardiac artefact ECG. This article explores the problem of removing ECG artefact from respiratory EMG signal. The new method uses an adaptive structure with an electrocardyographic ECG reference signal carried out by wavelet decomposition. The proposed algorithm requires only one channel to both estimating the adaptive filter input reference noise and the respiratory EMG signal. This new technique demonstrates how two steps will be combined: the first step decomposes the signal with forward discrete wavelet transform into sub-bands to get the wavelet coefficients. Then, an improved soft thresholding function was applied. And the ECG input reference signal is reconstructed with the transformed coefficients whereas, the second uses an adaptive filter especially the LMS one to remove the ECG signal. After trying statistical as well as mathematical analysis, the complete investigation ensures that all details and steps make proof that our rigorous method is appropriate. Compared to the results obtained using previous techniques, the results achieved using the new algorithm show a significant improvement in the efficiency of the ECG rejection.     

Ultrasonic Elastography Features of Phyllodes Tumors of the Breast: A Clinical Research

The purpose of this study was to analyze the ultrasonic elastography features of phyllodes tumors of the breast comparing with fibroadenomas. A retrospective database was queried for the patients diagnosed as phyllodes tumors and fibroadenomas at Sun Yat-sen Memorial Hospital from January 2008 to August 2012. Three hundred and fifty lesions from 323 consecutive patients were included in the study. All the cases were examined by conventional ultrasonography and ultrasound elastography. Ultrasound elastography was used to calculate strain ratio of the lesions with bilateral breast tissue at the same depth as reference. There were 36 phyllodes tumors (27 benign, 8 borderline, 1 malignant) and 314 fibroadenomas (158 the pericanalicular type, 103 the intracanalicular type, 53 other special types). The strain ratio for phyllodes tumors (3.19±2.33) was significantly higher than for fibroadenomas (1.69±0.88) (p<0.05). The Spearman^.s correlation coefficient between strain ratio of ultrasound elastography and pathological groups was significant, with a value of 0.17 (p<0.05). Ultrasound elastography could provide additional information to differentiate phyllodes tumors from fibroadenoma in breast.     

基于MATLAB的乳腺钼靶图像直方图增强方法的比较

目的比较不同直方图增强方法对改善乳腺铝靶图像质量的效果。方法基于MATLAB编程,分别采用直方图均衡化（HE）、直方图规定化（HS）和对比度受限自适应直方图均衡化（CLAHE）对乳腺钼靶图像进行增强处理;利用峰值信噪比（PSNR）客观评价图像的噪声水平。结果HE对图像对比度的增强效果一般,图像细节反而有所下降;HS可选择匹配直方图函数的类型,从而选择性增强所需灰度范围,但图像噪声水平在三种方法中最高;CLAHE能很好地增强图像各部分的对比度,且图像噪声水平最低。结论应用直方图增强方法处理乳腺钼靶图像,在图像噪声水平和灰度增强上,CLAHE明显优于HE和HS。     

EMG signals detection and processing for on-line control of functional electrical stimulation.

The surface EMG signal detected from voluntarily activated muscles can be used as a control signal for functional neuromuscular electrical stimulation. A proper positioning of the recording electrodes in relation to the stimulation electrodes, and a proper processing of the recorded signals is required to reduce the stimulus artefact and the non-voluntary contribution (M-wave). Six orientations and six locations of the recording electrodes were investigated in the present work. A comb filter (with and without a blanking windowing) was applied to remove the signal components synchronously correlated to the stimulus. An operative definition of the signal to noise ratio and an efficiency index were implemented. It resulted that when the recording electrodes were located within the two stimulation electrodes the best orientation was perpendicular to the longitudinal line. However the best absolute indexes were obtained when the recording electrodes were located externally of the stimulation electrodes, and in that case the best orientation was longitudinal. Concerning the filtering procedure, the use of a blanking window before the application of the comb filter, gave the best performance.     

Evaluation of Neoadjuvant Chemotherapy Response in Women with Locally Advanced Breast Cancer Using Ultrasound Elastography

PURPOSE: Ultrasound elastography is a new imaging technique that can be used to assess tissue stiffness. The aim of this study was to investigate the potential of ultrasound elastography for monitoring treatment response of locally advanced breast cancer patients undergoing neoadjuvant therapy. METHODS: Fifteen women receiving neoadjuvant chemotherapy had the affected breast scanned before, 1, 4, and 8 weeks following therapy initiation, and then before surgery. Changes in elastographic parameters related to tissue biomechanical properties were then determined and compared to clinical and pathologic tumor response after mastectomy. RESULTS: Patients who responded to therapy demonstrated a significant decrease (P < .05) in strain ratios and strain differences 4 weeks after treatment initiation compared to non-responding patients. Mean strain ratio and mean strain difference for responders was 81 ± 3% and 1 ± 17% for static regions of interest (ROIs) and 81 ± 3% and 6 ± 18% for dynamic ROIs, respectively. In contrast, these parameters were 102±2%, 110±17%, 101±4%, and 109±30% for non-responding patients, respectively. Strain ratio using static ROIs was found to be the best predictor of treatment response, with 100% sensitivity and 100% specificity obtained 4 weeks after starting treatment. CONCLUSIONS: These results suggest that ultrasound elastography can be potentially used as an early predictor of tumor therapy response in breast cancer patients.     

A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS–wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).     

Analysis of Video-Based Microscopic Particle Trajectories Using Kalman Filtering

The fidelity of the trajectories obtained from video-based particle tracking determines the success of a variety of biophysical techniques, including in situ single cell particle tracking and in vitro motility assays. However, the image acquisition process is complicated by system noise, which causes positioning error in the trajectories derived from image analysis. Here, we explore the possibility of reducing the positioning error by the application of a Kalman filter, a powerful algorithm to estimate the state of a linear dynamic system from noisy measurements. We show that the optimal Kalman filter parameters can be determined in an appropriate experimental setting, and that the Kalman filter can markedly reduce the positioning error while retaining the intrinsic fluctuations of the dynamic process. We believe the Kalman filter can potentially serve as a powerful tool to infer a trajectory of ultra-high fidelity from noisy images, revealing the details of dynamic cellular processes.     

Ultrasound elastographic assessment of plantar fascia in runners using rearfoot strike and forefoot strike

Forefoot strike is increasingly being adopted by runners because it can better attenuate impact than rearfoot strike. However, forefoot strike may overload the plantar fascia and alter the plantar fascia elasticity. This study aimed to use ultrasound elastography to investigate and compare shear wave elasticity of the plantar fascia between rearfoot strikers and forefoot strikers. A total of 35 participants (21 rearfoot strikers and 14 forefoot strikers), who were free of lower limb injuries and diseases, were recruited from a local running club. Individual foot strike patterns were identified through the measured plantar pressure during treadmill running. The B-Mode ultrasound images and shear wave elastographic images of the plantar fascia were collected from each runner. Two independent investigators reviewed the images and examined the plantar fascia qualitatively and quantitatively. The results demonstrated an overall good agreement between the investigators in the image review outcomes (ICC:0.96–0.98, κ: 0.89). There were no significant differences in the fascial thickness (p = 0.50) and hypoechogenicity on the gray-scale images (p = 0.54) between the two groups. Shear wave elastography showed that forefoot strikers exhibited reduced plantar fascia elasticity compared to rearfoot strikers (p = 0.01, Cohen’s d = 0.91). A less elastic fascial tissue was more easily strained under loading. Tissue overstrain is frequently related to the incidence of plantar fasciitis. While further study is needed for firm conclusions, runners using forefoot strike were encouraged to enhance their foot strength for better protection of the plantar fascia.     

Local estimate of surface Laplacian derivation on a realistically shaped scalp surface and its performance on noisy data

A new implementation of the surface Laplacian derivation (SLD) method is desribed which reconstructs a realistically shaped, local scalp surface geometry using measured electrode positions, generates a local spectral-interpolated potential distribution function, and estimates the surface Laplacian values through a local planar parametric space using a stable numerical method combining Taylor expansions with the least-squares technique. The implementation is modified for efficient repeated SLD operations on a time series. Examples are shown of applications to evoked potential data. The resolving power of the SLD is examined as a function of the spatial signal-to-noise (SNR) ratio. The analysis suggests that the Laplacian is effective when the spatial SNR is greater than 3. It is shown that spatial low-pass filtering with a Gaussian filter can be used to reduce the effect of noise and recover useful signal if the noise is spatially incoherent.     

Speckle reducing bilateral filter for cattle follicle segmentation

Background

Ultrasound imaging technology has wide applications in cattle reproduction and has been used to monitor individual follicles and determine the patterns of follicular development. However, the speckles in ultrasound images affect the post-processing, such as follicle segmentation and finally affect the measurement of the follicles. In order to reduce the effect of speckles, a bilateral filter is developed in this paper.

Results

We develop a new bilateral filter for speckle reduction in ultrasound images for follicle segmentation and measurement. Different from the previous bilateral filters, the proposed bilateral filter uses normalized difference in the computation of the Gaussian intensity difference. We also present the results of follicle segmentation after speckle reduction. Experimental results on both synthetic images and real ultrasound images demonstrate the effectiveness of the proposed filter.

Conclusions

Compared with the previous bilateral filters, the proposed bilateral filter can reduce speckles in both high-intensity regions and low intensity regions in ultrasound images. The segmentation of the follicles in the speckle reduced images by the proposed method has higher performance than the segmentation in the original ultrasound image, and the images filtered by Gaussian filter, the conventional bilateral filter respectively.

     

Transversely isotropic elasticity imaging of cancellous bone

To measure spatial variations in mechanical properties of biological materials, prior studies have typically performed mechanical tests on excised specimens of tissue. Less invasive measurements, however, are preferable in many applications, such as patient-specific modeling, disease diagnosis, and tracking of age- or damage-related degradation of mechanical properties. Elasticity imaging (elastography) is a nondestructive imaging method in which the distribution of elastic properties throughout a specimen can be reconstructed from measured strain or displacement fields. To date, most work in elasticity imaging has concerned incompressible, isotropic materials. This study presents an extension of elasticity imaging to three-dimensional, compressible, transversely isotropic materials. The formulation and solution of an inverse problem for an anisotropic tissue subjected to a combination of quasi-static loads is described, and an optimization and regularization strategy that indirectly obtains the solution to the inverse problem is presented. Several applications of transversely isotropic elasticity imaging to cancellous bone from the human vertebra are then considered. The feasibility of using isotropic elasticity imaging to obtain meaningful reconstructions of the distribution of material properties for vertebral cancellous bone from experiment is established. However, using simulation, it is shown that an isotropic reconstruction is not appropriate for anisotropic materials. It is further shown that the transversely isotropic method identifies a solution that predicts the measured displacements, reveals regions of low stiffness, and recovers all five elastic parameters with approximately 10% error. The recovery of a given elastic parameter is found to require the presence of its corresponding strain (e.g., a deformation that generates ??? is necessary to reconstruct C????), and the application of regularization is shown to improve accuracy. Finally, the effects of noise on reconstruction quality is demonstrated and a signal-to-noise ratio (SNR) of 40 dB is identified as a reasonable threshold for obtaining accurate reconstructions from experimental data. This study demonstrates that given an appropriate set of displacement fields, level of regularization, and signal strength, the transversely isotropic method can recover the relative magnitudes of all five elastic parameters without an independent measurement of stress. The quality of the reconstructions improves with increasing contrast, magnitude of deformation, and asymmetry in the distributions of material properties, indicating that elasticity imaging of cancellous bone could be a useful tool in laboratory studies to monitor the progression of damage and disease in this tissue.     

基于改进型组织特异纹理滤波器的乳腺肿瘤超声图像处理

基于乳腺超声图像的多参数纹理分类实验,改进了Gjenna Sfippel等的自适应纹理滤波器,通过引入模糊函数、增加重叠区域和迭代次数的措施,在减少图像噪声的同时,增强肿瘤与周围正常组织的视觉差别。量化比较乳腺超声图像经该滤波算法和几种常用滤波算法处理前后的的统计特征参量和肿瘤边缘检测的精确率,验证了该算法的有效性和优越性。     

The use of 2D ultrasound elastography for measuring tendon motion and strain

The goal of the current study was to investigate the fidelity of a 2D ultrasound elastography method for the measurement of tendon motion and strain. Ultrasound phantoms and ex vivo porcine flexor tendons were cyclically stretched to 4% strain while cine ultrasound radiofrequency (RF) data and video data were simultaneously collected. 2D ultrasound elastography was used to estimate tissue motion and strain from RF data, and surface tissue motion and strain were separately estimated using digital image correlation (DIC). There were strong correlations (R²>0.97) between DIC and RF measurements of phantom displacement and strain, and good agreement in estimates of peak phantom strain (DIC: 3.5±0.2%; RF: 3.7±0.1%). For tendon, elastographic estimates of displacement profiles also correlated well with DIC measurements (R²>0.92), and exhibited similar estimated peak tendon strain (DIC: 2.6±1.4%; RF: 2.2±1.3%). Elastographic tracking with B-Mode images tended to under-predict peak strain for both the phantom and tendon. This study demonstrates the capacity to use quantitative elastographic techniques to measure tendon displacement and strain within an ultrasound image window. The approach may be extendible to in vivo use on humans, which would allow for the non-invasive analysis of tendon deformation in both normal and pathological states.     

Interpolated Compressed Sensing for 2D Multiple Slice Fast MR Imaging

Sparse MRI has been introduced to reduce the acquisition time and raw data size by undersampling the k-space data. However, the image quality, particularly the contrast to noise ratio (CNR), decreases with the undersampling rate. In this work, we proposed an interpolated Compressed Sensing (iCS) method to further enhance the imaging speed or reduce data size without significant sacrifice of image quality and CNR for multi-slice two-dimensional sparse MR imaging in humans. This method utilizes the k-space data of the neighboring slice in the multi-slice acquisition. The missing k-space data of a highly undersampled slice are estimated by using the raw data of its neighboring slice multiplied by a weighting function generated from low resolution full k-space reference images. In-vivo MR imaging in human feet has been used to investigate the feasibility and the performance of the proposed iCS method. The results show that by using the proposed iCS reconstruction method, the average image error can be reduced and the average CNR can be improved, compared with the conventional sparse MRI reconstruction at the same undersampling rate.     

Minimum mean square error estimation of connectivity in biological neural networks

A minimum mean square error (MMSE) estimation scheme is employed to identify the synaptic connectivity in neural networks. This new approach can substantially reduce the amount of data and the computational cost involved in the conventional correlation methods, and is suitable for both nonstationary and stationary neuronal firings. Two algorithms are proposed to estimate the synaptic connectivities recursively, one for nonlinear filtering, the other for linear filtering. In addition, the lower and upper bounds for the MMSE estimator are determined. It is shown that the estimators are consistent in quadratic mean. We also demonstrate that the conventional cross-interval histogram is an asymptotic linear MMSE estimator with an inappropriate initial value. Finally, simulations of both nonlinear and linear (Kalman filter) estimates demonstrate that the true connectivity values are approached asymptotically.     

On the causes,consequences, and avoidance of PCR duplicates: Towards a theory of library complexity

Library preparation protocols for most sequencing technologies involve PCR amplification of the template DNA, which open the possibility that a given template DNA molecule is sequenced multiple times. Reads arising from this phenomenon, known as PCR duplicates, inflate the cost of sequencing and can jeopardize the reliability of affected experiments. Despite the pervasiveness of this artefact, our understanding of its causes and of its impact on downstream statistical analyses remains essentially empirical. Here, we develop a general quantitative model of amplification distortions in sequencing data sets, which we leverage to investigate the factors controlling the occurrence of PCR duplicates. We show that the PCR duplicate rate is determined primarily by the ratio between library complexity and sequencing depth, and that amplification noise (including in its dependence on the number of PCR cycles) only plays a secondary role for this artefact. We confirm our predictions using new and published RAD-seq libraries and provide a method to estimate library complexity and amplification noise in any data set containing PCR duplicates. We discuss how amplification-related artefacts impact downstream analyses, and in particular genotyping accuracy. The proposed framework unites the numerous observations made on PCR duplicates and will be useful to experimenters of all sequencing technologies where DNA availability is a concern.     

超声弹性成像与超声造影对肝肿瘤的诊断效果对比

目的:分析超声弹性成像与超声造影对肝肿瘤的诊断效果。方法:收集我院2015年3月至2016年3月收治的肝肿瘤患者76例,术前均行超声弹性成像和超声造影检查,比较超声弹性成像和超声造影与病理诊断(黄金标准)的结果。结果:超声弹性成像与病理检查结果比较无统计学差异(P0.05);超声造影与病理检查结果无统计学差异(P0.05);超声弹性成像和超声造影的敏感性、特异性、准确性无统计学差异(P0.05)。结论:超声弹性成像、超声造影对肝肿瘤诊断中均有重要价值,建议二者联合检测,提高肝肿瘤检出准确率。     

Detection and correction of probe-level artefacts on microarrays

ABSTRACT: BACKGROUND: A recent large-scale analysis of Gene Expression Omnibus (GEO) data found frequent evidence for spatial defects in a substantial fraction of Affymetrix microarrays in the GEO. Nevertheless, in contrast to quality assessment, artefact detection is not widely used in standard gene expression analysis pipelines. Furthermore, although approaches have been proposed to detect diverse types of spatial noise on arrays, the correction of these artefacts is mostly left to either summarization methods or the corresponding arrays are completely discarded. RESULTS: We show that state-of-the-art robust summarization procedures are vulnerable to artefacts on arrays and cannot appropriately correct for these. To address this problem, we present a simple approach to detect artefacts with high recall and precision, which we further improve by taking into account the spatial layout of arrays. Finally, we propose two correction methods for these artefacts that either substitute values of defective probes using probeset information or filter corrupted probes. We show that our approach can identify and correct defective probe measurements appropriately and outperforms existing tools. CONCLUSIONS: While summarization is insufficient to correct for defective probes, this problem can be addressed in a straightforward way by the methods we present for identification and correction of defective probes. As these methods output CEL files with corrected probe values that serve as input to standard normalization and summarization procedures, they can be easily integrated into existing microarray analysis pipelines as an additional pre-processing step. An R package is freely available from http://www.bio.ifi.lmu.de/artefact-correction.     

Blind source separation,wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling

This paper proposes an automatic method for artefact removal and noise elimination from scalp electroencephalogram recordings (EEG). The method is based on blind source separation (BSS) and supervised classification and proposes a combination of classical and news features and classes to improve artefact elimination (ocular, high frequency muscle and ECG artefacts). The role of a supplementary step of wavelet denoising (WD) is explored and the interactions between BSS, denoising and classification are analyzed. The results are validated on simulated signals by quantitative evaluation criteria and on real EEG by medical expertise. The proposed methodology successfully rejected a good percentage of artefacts and noise, while preserving almost all the cerebral activity. The “denoised artefact-free” EEG presents a very good improvement compared with recorded raw EEG: 96% of the EEGs are easier to interpret.