The Shenzhen Key Laboratory for Low cost Healthcare

Shenzhen, China

The Shenzhen Key Laboratory for Low cost Healthcare

Shenzhen, China

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Ling S.,Central South University | Ling S.,The Shenzhen Key Laboratory for Low cost Healthcare | Ling S.,CAS Shenzhen Institutes of Advanced Technology | Chen B.,The Shenzhen Key Laboratory for Low cost Healthcare | And 10 more authors.
BioMedical Engineering Online | Year: 2013

Background: Muscle fiber orientation (MFO) is an important parameter related to musculoskeletal functions. The traditional manual method for MFO estimation in sonograms was labor-intensive. The automatic methods proposed in recent years also involved voting procedures which were computationally expensive.Methods: In this paper, we proposed a new framework to efficiently estimate MFO in sonograms. We firstly employed Multi-scale Vessel Enhancement Filtering (MVEF) to enhance fascicles in the sonograms and then the enhanced images were binarized. Finally, line-shaped patterns in the binary map were detected one by one, according to their shape properties. Specifically speaking, for the long-and-thinner regions, the orientation of the targeted muscle fibre was directly computed, without voting procedures, as the orientation of the ellipse that had the same normalized second central moments as the region. For other cases, the Hough voting procedure might be employed for orientation estimation. The performance of the algorithm was evaluated using four various group of sonograms, which are a dataset used in previous reports, 33 sonograms of gastrocnemius from 11 young healthy subjects, one sonogram sequence including 200 frames from a subject and 256 frames from an aged subject with cerebral infarction respectively.Results: It was demonstrated in the experiments that measurements of the proposed method agreed well with those of the manual method and achieved much more efficiency than the previous Re-voting Hough Transform (RVHT) algorithm.Conclusions: Results of the experiments suggested that, without compromising the accuracy, in the proposed framework the previous orientation estimation algorithm was accelerated by reduction of its dependence on voting procedures. © 2013 Ling et al.; licensee BioMed Central Ltd.


Han P.,CAS Shenzhen Institutes of Advanced Technology | Han P.,The Shenzhen Key Laboratory for Low cost Healthcare | Chen Y.,CAS Shenzhen Institutes of Advanced Technology | Chen Y.,The Shenzhen Key Laboratory for Low cost Healthcare | And 10 more authors.
BioMedical Engineering Online | Year: 2013

Background: Ultrasonography is a convenient technique to investigate muscle properties and has been widely used to look into muscle functions since it is non-invasive and real-time. Muscle thickness, a quantification which can effectively reflect the muscle activities during muscle contraction, is an important measure for musculoskeletal studies using ultrasonography. The traditional manual operation to read muscle thickness is subjective and time-consuming, therefore a number of studies have focused on the automatic estimation of muscle fascicle orientation and muscle thickness, to which the speckle noises in ultrasound images could be the major obstacle. There have been two popular methods proposed to enhance the hyperechoic regions over the speckles in ultrasonography, namely Gabor Filtering and Multiscale Vessel Enhancement Filtering (MVEF).Methods: A study on gastrocnemius muscle is conducted to quantitatively evaluate whether and how these two methods could help the automatic estimation of the muscle thickness based on Revoting Hough Transform (RVHT). The muscle thickness results obtained from each of the two methods are compared with the results from manual measurement, respectively. Data from an aged subject with cerebral infarction is also studied.Results: It's shown in the experiments that, Gabor Filtering and MVEF can both enable RVHT to generate comparable results of muscle thickness to those by manual drawing (mean ± SD, 1.45 ± 0.48 and 1.38 ± 0.56 mm respectively). However, the MVEF method requires much less computation than Gabor Filtering.Conclusions: Both methods, as preprocessing procedure can enable RVHT the automatic estimation of muscle thickness and MVEF is believed to be a better choice for real-time applications. © 2013 Han et al; licensee BioMed Central Ltd.


Wen T.,CAS Shenzhen Institutes of Advanced Technology | Wen T.,University of Chinese Academy of Sciences | Wen T.,The Shenzhen Key Laboratory for Low cost Healthcare | Yang F.,Southern Medical University | And 4 more authors.
Neurocomputing | Year: 2015

Freehand three-dimensional (3D) ultrasound imaging is an important medical imaging modality in computer-assisted clinical diagnosis and image-guided intervention. In this paper, we present a novel Bayesian-based nonlocal method for the accurate volume reconstruction of freehand 3D ultrasound imaging with irregularly spaced B-scans. In the algorithm, each pixel is represented as the Gamma distribution which corresponds to the speckle noise generated by the interaction of the acoustic wave with the tissues. The variational reconstruction functional is associated with a nonlocal denoising term and a nonlocal inpainting term. To suppress speckle noise in the ultrasound image, the observed data is filtered via nonlocal total variation method firstly. The nonlocal denoising model is adapted to the speckle noise by substituting the Pearson distance-based weight function for the Gaussian weight function. To interpolate the missing data, a new inpainting scheme derived from the nonlocal means filter and its implementation based on fast marching method are introduced to fill the empty regions. This makes interpolation of missing data more accurate and effective. The Pearson distance function derived from the Bayesian estimator is not only used for speckle reduction, but also serves as weight function for building nonlocal means-based inpainting algorithm. Experimental results on synthetic cube data, in-vitro ultrasound abdominal phantom and in-vivo liver of human subject and comparisons with some classical and recent algorithms are used to demonstrate its improvement in both speckle suppression and edge preservation in 3D ultrasound reconstruction. © 2015 Elsevier B.V.


Zhou Y.,CAS Shenzhen Institutes of Advanced Technology | Zhou Y.,The Shenzhen Key Laboratory for Low cost Healthcare | Zhou Y.,Hong Kong Polytechnic University | Li J.-Z.,CAS Shenzhen Institutes of Advanced Technology | And 4 more authors.
BioMedical Engineering Online | Year: 2012

Background: Muscle fascicle pennation angle (PA) is an important parameter related to musculoskeletal functions, and ultrasound imaging has been widely used for measuring PA, but manually and frame by frame in most cases. We have earlier reported an automatic method to estimate aponeurosis orientation based on Gabor transform and Revoting Hough Transform (RVHT).Methods: In this paper, we proposed a method to estimate the overall orientation of muscle fascicles in a region of interest, in order to complete computing the orientation of the other side of the pennation angle, but the side found by RVHT. The measurements for orientations of both fascicles and aponeurosis were conducted in each frame of ultrasound images, and then the dynamic change of pennation angle during muscle contraction was obtained automatically. The method for fascicle orientation estimation was evaluated using synthetic images with different noise levels and later on 500 ultrasound images of human gastrocnemius muscles during isometric plantarflexion.Results: The muscle fascicle orientations were also estimated manually by two operators. From the results it's found that the proposed automatic method demonstrated a comparable performance to the manual method.Conclusions: With the proposed methods, ultrasound measurement for muscle pennation angles can be more widely used for functional assessment of muscles. © 2012 Zhou et al.; licensee BioMed Central Ltd.


Chen X.,CAS Shenzhen Institutes of Advanced Technology | Chen X.,The Shenzhen Key Laboratory for Low cost Healthcare | Chen X.,South China Normal University | Wen T.,CAS Shenzhen Institutes of Advanced Technology | And 9 more authors.
BioMedical Engineering Online | Year: 2014

Introduction: Freehand three-dimensional (3D) ultrasound has the advantages of flexibility for allowing clinicians to manipulate the ultrasound probe over the examined body surface with less constraint in comparison with other scanning protocols. Thus it is widely used in clinical diagnose and image-guided surgery. However, as the data scanning of freehand-style is subjective, the collected B-scan images are usually irregular and highly sparse. One of the key procedures in freehand ultrasound imaging system is the volume reconstruction, which plays an important role in improving the reconstructed image quality.System and methods: A novel freehand 3D ultrasound volume reconstruction method based on kernel regression model is proposed in this paper. Our method consists of two steps: bin-filling and regression. Firstly, the bin-filling step is used to map each pixel in the sampled B-scan images to its corresponding voxel in the reconstructed volume data. Secondly, the regression step is used to make the nonparametric estimation for the whole volume data from the previous sampled sparse data. The kernel penalizes distance away from the current approximation center within a local neighborhood.Experiments and results: To evaluate the quality and performance of our proposed kernel regression algorithm for freehand 3D ultrasound reconstruction, a phantom and an in-vivo liver organ of human subject are scanned with our freehand 3D ultrasound imaging system. Root mean square error (RMSE) is used for the quantitative evaluation. Both of the qualitative and quantitative experimental results demonstrate that our method can reconstruct image with less artifacts and higher quality.Conclusion: The proposed kernel regression based reconstruction method is capable of constructing volume data with improved accuracy from irregularly sampled sparse data for freehand 3D ultrasound imaging system. © 2014 Chen et al.; licensee BioMed Central Ltd.


Wen T.,CAS Shenzhen Institutes of Advanced Technology | Wen T.,University of Chinese Academy of Sciences | Wen T.,The Shenzhen Key Laboratory for Low cost Healthcare | Zhu Q.,CAS Shenzhen Institutes of Advanced Technology | And 3 more authors.
Applied Mathematics and Information Sciences | Year: 2013

Scale-spaces play an important role in many computer vision tasks. Automatic scale selection is at the foundation of multiscale image analysis, but its performance is still very subjective and empirical. To automatically select the appropriate scale for a particular issue, a scale selection model based on information theory is proposed in this paper. The proposed model utilizes mutual information as a measuring criterion of similarity for the optimal scale selection in multi-scale analysis, with applications to image denoising and segmentation. Firstly, we focus on the morphological operator based scale selection to image denoising. This technique does not require the prior knowledge of the noise variance and can effectively eliminate the variation of illumination. Secondly, we develop a clustering based unsupervised image segmentation algorithm by recursively pruning the Huffman coding tree. The proposed clustering algorithm can preserve the maximum amount of information at a specific clustering number from the information-theoretical point of view. Finally, for the feasibility of the proposed algorithms, its theoretical properties are analyzed mathematically and its performance is tested by a series of experiments, which demonstrate that it yields the optimal scale for our developed image denoising and segmentation algorithms. © 2013 NSP Natural Sciences Publishing Cor.


Nie Z.,CAS Shenzhen Institutes of Advanced Technology | Nie Z.,The Shenzhen Key Laboratory for Low cost Healthcare | Nie Z.,University of Chinese Academy of Sciences | Guan F.,CAS Shenzhen Institutes of Advanced Technology | And 3 more authors.
China Communications | Year: 2012

Human body communication is proposed as a promising body proximal communication technology for body sensor networks. To achieve low power and small volume in the sensor nodes, a Radio Frequency (RF) application-specific integrated circuit transceiver for Human Body Communication (HBC) is presented and the characteristics of HBC are investigated. A high data rate On-Off Keying (OOK)/Frequency-Shift Keying (FSK) modulation protocol and an OOK/FSK demodulator circuit are introduced in this paper, with a data-rate-to-carrier-frequency ratio up to 70%. A low noise amplifier is proposed to handle the dynamic range problem and improve the sensitivity of the receiver path. In addition, a low power automatic-gain-control system is realized using a novel architecture, thereby rendering the peak detector circuit and loop filter unnecessary. Finally, the complete chip is fabricated. Simulation results suggest receiver sensitivity to be -75 dBm. The transceiver shows an overall power consumption of 3.2 mW when data rate is 5 Mbps, delivering a P1dB output power of -30 dBm.


PubMed | The Shenzhen Key Laboratory for Low cost Healthcare
Type: | Journal: Biomedical engineering online | Year: 2013

Muscle fiber orientation (MFO) is an important parameter related to musculoskeletal functions. The traditional manual method for MFO estimation in sonograms was labor-intensive. The automatic methods proposed in recent years also involved voting procedures which were computationally expensive.In this paper, we proposed a new framework to efficiently estimate MFO in sonograms. We firstly employed Multi-scale Vessel Enhancement Filtering (MVEF) to enhance fascicles in the sonograms and then the enhanced images were binarized. Finally, line-shaped patterns in the binary map were detected one by one, according to their shape properties. Specifically speaking, for the long-and-thinner regions, the orientation of the targeted muscle fibre was directly computed, without voting procedures, as the orientation of the ellipse that had the same normalized second central moments as the region. For other cases, the Hough voting procedure might be employed for orientation estimation. The performance of the algorithm was evaluated using four various group of sonograms, which are a dataset used in previous reports, 33 sonograms of gastrocnemius from 11 young healthy subjects, one sonogram sequence including 200 frames from a subject and 256 frames from an aged subject with cerebral infarction respectively.It was demonstrated in the experiments that measurements of the proposed method agreed well with those of the manual method and achieved much more efficiency than the previous Re-voting Hough Transform (RVHT) algorithm.Results of the experiments suggested that, without compromising the accuracy, in the proposed framework the previous orientation estimation algorithm was accelerated by reduction of its dependence on voting procedures.

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