Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging

Laboratory for, China

Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging

Laboratory for, China
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Dai J.,Jiangsu University | Dai J.,Nanjing Southeast University | Bao X.,Jiangsu University | Xu W.,Guangdong University of Technology | And 2 more authors.
IEEE Signal Processing Letters | Year: 2017

The performance of the existing sparse Bayesian learning (SBL) methods for off-grid direction-of-arrival (DOA) estimation is dependent on the tradeoff between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, which adopts a coarse grid and considers the sampled locations in the coarse grid as the adjustable parameters. We utilize an expectation-maximization algorithm to iteratively refine this coarse grid and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced, and the modeling error can be almost eliminated. © 1994-2012 IEEE.


Wen Y.-T.,Shenzhen University | Lei H.-J.,Shenzhen University | You Z.-H.,Chinese Academy of Sciences | Lei B.-Y.,Shenzhen University | And 4 more authors.
Journal of Theoretical Biology | Year: 2017

Prediction of protein-protein interactions (PPIs) is of great significance. To achieve this, we propose a novel computational method for PPIs prediction based on a similarity network fusion (SNF) model for integrating the physical and chemical properties of proteins. Specifically, the physical and chemical properties of protein are the protein amino acid mutation rate and its hydrophobicity, respectively. The amino acid mutation rate is extracted using a BLOSUM62 matrix, which puts the protein sequence into block substitution matrix. The SNF model is exploited to fuse protein physical and chemical features of multiple data by iteratively updating each original network. Finally, the complementary features from the fused network are fed into a label propagation algorithm (LPA) for PPIs prediction. The experimental results show that the proposed method achieves promising performance and outperforms the traditional methods for the public dataset of H. pylori, Human, and Yeast. In addition, our proposed method achieves average accuracy of 76.65%, 81.98%, 84.56%, 84.01% and 84.38% on E. coli, C. elegans, H. sapien, H. pylori and M. musculus datasets, respectively. Comparison results demonstrate that the proposed method is very promising and provides a cost-effective alternative for predicting PPIs. The source code and all datasets are available at http://pan.baidu.com/s/1dF7rp7N. © 2017 Elsevier Ltd


Wang L.,Xiamen University | Li X.,Xiamen University | Chen Y.,Xiamen University | Qin J.,Shenzhen University | Qin J.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Inverse problem of electrocardiography (ECG) has been extensively investigated as the estimated epicardial potentials (EPs) reflecting underlying myocardial activities. Traditionally, L2-norm regularization methods have been proposed to solve this ill-posed problem. But L2-norm penalty function inherently leads to considerable smoothing of the solution, which reduces the accuracy of distinguishing abnormalities and locating diseased regions. Directly using L1-norm penalty function, however, may greatly increase the computational complexity due to its non-differentiability. In this study, we present a smoothed L0 norm technique in order to directly solve the L0 norm constrained problem. Our method employs a smoothing function to make the L0 norm continuous. Extensive experiments on various datasets, including normal human data, isolated canine data, and WPW syndrome data, were conducted to validate our method. Epicardial potentials mapped during pacing were also reconstructed and visualized on the heart surface. Experimental results show that the proposed method reconstructs more accurate epicardial potentials compared with L1 norm and L2 norm based methods, demonstrating that smoothed L0 norm is a promising method for the noninvasive estimation of epicardial potentials. © Springer International Publishing Switzerland 2015.


Dan G.,Shenzhen University | Dan G.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging | Dan G.,National Reginoal Key Technology Engineering Laboratory for Medical Ultrasound | Liu Z.-W.,South China University of Technology | And 2 more authors.
Gaoya Wuli Xuebao/Chinese Journal of High Pressure Physics | Year: 2015

The particle and stability properties of phosphatides dispersions affected by pulsed electric field, ultrasound, microfluid were studied using dynamic light scattering techniques. Results indicate that the average diameter of phosphatides particles decrease from 594.4 nm to 259.2, 88.7, 37.8 nm after being treated by 60 kV/cm pulsed electric field, 500 W ultrasound and 130 MPa microfluid, respectively. As for the stability of the treated phosphatides dispersion, it is demonstrated that the ultrasound treated sample is the most stable one, then following microfluid and pulsed electric field treated samples. ©, 2015, Chinese Journal of High Pressure Physics. All right reserved.


Li J.,Shenzhen University | Li J.,Chinese University of Hong Kong | Zhou Y.,Shenzhen University | Zhou Y.,National Regional Key Technology Engineering Laboratory for Medical Ultrasound | And 3 more authors.
Ultrasonics | Year: 2015

Abstract Muscle force output is an essential index in rehabilitation assessment or physical exams, and could provide considerable insights for various applications such as load monitoring and muscle assessment in sports science or rehabilitation therapy. Besides direct measurement of force output using a dynamometer, electromyography has earlier been used in several studies to quantify muscle force as an indirect means. However, its spatial resolution is easily compromised as a summation of the action potentials from neighboring motor units of electrode site. To explore an alternative method to indirectly estimate the muscle force output, and with better muscle specificity, we started with an investigation on the relationship between architecture dynamics and force output of triceps surae. The muscular architecture dynamics is captured in ultrasonography sequences and estimated using a previously reported motion estimation method. Then an indicator named as the dorsoventrally averaged motion profile (DAMP) is employed. The performance of force output is represented by an instantaneous version of the rate of force development (RFD), namely I-RFD. From experimental results on ten normal subjects, there were significant correlations between the I-RFD and DAMP for triceps surae, both normalized between 0 and 1, with the sum of squares error at 0.0516±0.0224, R-square at 0.7929±0.0931 and root mean squared error at 0.0159±0.0033. The statistical significance results were less than 0.01. The present study suggested that muscle architecture dynamics extracted from ultrasonography during contraction is well correlated to the I-RFD and it can be a promising option for indirect estimation of muscle force output. © 2015 Elsevier B.V.


Dan G.,Shenzhen University | Dan G.,National Regional Key Technology Engineering Laboratory for Medical Ultrasound | Dan G.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging | Li Z.,Shenzhen University | And 3 more authors.
IFMBE Proceedings | Year: 2015

In recent decades, a new attempt at estimating respiratory rate (RR) from Photoplethysmogram (PPG) becomes an active area. It can be implemented by different algorithms among of which wavelet based methods are commonly used with good performances achieved. However, the study on the reason why different mother wavelets have different performances on RR estimation as well as how a suitable wavelet can be easily selected is insufficient. In this paper, a mother wavelet selection algorithm is proposed for RR estimation from PPG signal. Six popular mother wavelets, namely db10, db12, sym8, bior6.8, rbio6.8 and coif5, are compared in terms of the sum of decomposition coefficient magnitudes and the one with maximum value is chosen for RR information extraction and reconstruction. In the experiments, the proposed algorithm is compared with the related six mother wavelets working separately. Two evaluation tools, root mean squared normalized error (RMSNE) and Bland & Altman plot, are adopted. The evaluation results demonstrate the better performance of the proposed algorithm. In addition, the above finding reveals that the mother wavelet with a larger sum of coefficient magnitudes has a better performance on RR estimation from PPG signal which can be used as wavelet selection criteria in this area. © Springer International Publishing Switzerland 2015.


Li B.,Shenzhen University | Li B.,National Regional Key Technology Engineering Laboratory for Medical Ultrasound | Li B.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging | Xu G.,CAS Shenzhen Institutes of Advanced Technology | And 4 more authors.
Medical Physics | Year: 2015

Purpose:Wireless capsule endoscopy (WCE) opens a newdoor for the digestive tract examination and diagnosis. However, the examination of its video data is tedious. This study aims to assist a physician to interpret a WCE video by segmenting it into different anatomic parts in the digestive tract. Methods: A two level WCE video segmentation scheme is proposed to locate the boundary between the stomach, small intestine, and large intestine. In the rough level, the authors utilize color feature to draw a dissimilarity curve for a WCE video and obtain an approximate boundary. Meanwhile, training data for the fine level segmentation can be collected automatically between the two approximate boundaries of organs to overcome the difficulty of training data collection in traditional approaches. In the fine level, color histogram in the HSI color space is used to segment the stomach and small intestine. Then, color uniform local binary pattern (CULBP) algorithm is applied for discrimination of the small intestine and large intestine, which includes two patterns, namely, color norm and color angle pattern. The CULBP feature is robust to variation of illumination and discriminative for classification. In order to increase the performance of support vector machine, the authors integrate it with the Adaboost approach. Finally, the authors refine the classification results to segment a WCE video into different parts, that is, the stomach, small intestine, and large intestine. Results: The average precision and recall are 91.2% and 90.6% for the stomach/small intestine classification, 89.2% and 88.7% for the small/large intestine discrimination. Paired t-test also demonstrates a significant better performance of the proposed scheme compared to some traditional methods. The average segmentation error is 8 frames for the stomach/small intestine discrimination, and 14 frames for the small/large intestine segmentation. Conclusions: The results have demonstrated that the new video segmentation method can accurately locate the boundary between different organ regions in a WCE video. Such a segmentation result may enhance the efficiency of WCE examination. © 2015 American Association of Physicists in Medicine.


Chen X.,Shenzhen University | Chen X.,National Regional Key Technology Engineering Laboratory for Medical Ultrasound | Chen X.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging | Shen Y.,Shenzhen University | And 15 more authors.
Ultrasound in Medicine and Biology | Year: 2013

Ultrasound elastography, based on shear wave propagation, enables the quantitative and non-invasive assessment of liver mechanical properties such as stiffness and has been found to be feasible for and useful in the diagnosis of hepatic fibrosis. Most ultrasound elastographic methods use a purely elastic model to describe liver mechanical properties. However, to describe tissue that is dispersive and to obtain an accurate measure of tissue elasticity, the viscoelasticity of the tissue should be examined. The objective of this study was to investigate the shear viscoelastic characteristics, as measured by ultrasound elastography, of liver fibrosis in a rat model and to evaluate the diagnostic accuracy of viscoelasticity for staging liver fibrosis. Liver fibrosis was induced in 37 rats using carbon tetrachloride (CCl4); 6 rats served as controls. Liver viscoelasticity was measured invitro using shear waves induced by acoustic radiation force. The measured mean values of liver elasticity and viscosity ranged from 0.84 to 3.45kPa and from 1.12 to 2.06Pa·s for fibrosis stages F0-F4, respectively. Spearman correlation coefficients indicated that stage of fibrosis was well correlated with elasticity (0.88) and moderately correlated with viscosity (0.66). The areas under receiver operating characteristic curves were 0.97 (≥F2), 0.91 (≥F3) and 1.00(F4) for elasticity and 0.91 (≥F2), 0.79 (≥F3) and 0.74 (F4) for viscosity, respectively. The results confirmed that shear wave velocity was dispersive in frequency, suggesting a viscoelastic model to describe liver fibrosis. The study finds that although viscosity is not as good as elasticity for staging fibrosis, it is important to consider viscosity to make an accurate estimation of elasticity; it may also provide other mechanical insights into liver tissues. © 2013 World Federation for Ultrasound in Medicine & Biology.


Guo L.,Chinese Academy of Sciences | Guo L.,University of Chinese Academy of Sciences | Guo L.,China University of Petroleum - East China | Liu G.-Q.,Chinese Academy of Sciences | And 3 more authors.
Chinese Physics B | Year: 2014

Conductivities tomography with the interactions of magnetic field, electrical field, and ultrasound field is presented in this paper. We utilize a beam of ultrasound in scanning mode instead of the traditional ultrasound field generated by point source. Many formulae for the reconstruction of conductivities are derived from the voltage signals detected by two electrodes arranged somewhere on tissue's surface. In a forward problem, the numerical solutions of ultrasound fields generated by the piston transducer are calculated using the angular spectrum method and its Green's function is designed approximately in far fields. In an inverse problems, the magneto - acousto - electrical voltage signals are proved to satisfy the wave equations if the voltage signals are extended to the whole region from the boundary locations of transducers. Thus the time-reversal method is applied to reconstructing the curl of the reciprocal current density. In addition, a least square iteration method of recovering conductivities from reciprocal current densities is discussed. © 2014 Chinese Physical Society and IOP Publishing Ltd.


Song Y.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging | Ni D.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging | Zeng Z.,Guangdong Medical College | He L.,Guangdong Medical College | And 3 more authors.
Journal of Medical Imaging and Health Informatics | Year: 2014

In this paper, a new method for automatic vaginal bacteria cell segmentation and classification is proposed. Segmentation algorithm based on superpixel is first investigated to segment region of interest of the input image into cells. Feature extraction based on the segmented regions is trained by supervised deep learning method. Four types of different bacteria are studied for classification. Our experimental results show the classification result yields an accuracy of 99%, sensitivity of 100% and specificity of 98.04%. Compared to the state-ofthe- arts method, better segmentation results have been achieved. Furthermore, our comparative analysis also shows that deep learning method outperforms traditional methods such as neural network and support vector machine. © 2014 American Scientific Publishers All rights reserved.

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