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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yaw-Jiunn Chiou Jyh-Wen Chai Clayton Chi-Chang Chen Shih-Yu Chen Hsian-Min Chen Yen-Chieh Ouyang Wu-Chung Su Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices.

关键词： Training Support vector machine classification image classification Vectors Volume measurement Magnetic resonance

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ECG denoising using modulus maxima of wavelet transform

ECG denoising using modulus maxima of wavelet transform

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mohammad Ayat Mohammad B. Shamsollahi Behrooz Mozaffari Shahrzad Kharabian College of Engineering Department of Electrical Engineering American University of Sharjah UAE Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Iran

ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal.

关键词： Electrocardiography Noise reduction Wavelet transforms signal to noise ratio Gaussian noise biomedical engineering Noise level Distortion Wavelet analysis signal analysis

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Fetal R-wave detection from multichannel abdominal ECG recordings in low SNR

Fetal R-wave detection from multichannel abdominal ECG recor...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Shahrzad Kharabian Mohammad B. Shamsollahi Reza Sameni Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran School of Electrical & Computer Engineering Shiraz University Shiraz Iran

Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called piCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of piCA (after maternal ECG cancellation), had a very good performance. Also, as piCA ranks the components according to their resemblance to maternal ECG, it could be used in automatic fetal ECG extraction algorithms.

关键词： Abdomen Electrocardiography signal processing algorithms Fetal heart Data mining Independent component analysis Magnetic recording Interference Magnetic separation Electrodes

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Detection of characteristic points of ECG using quadratic spline wavelet transfrom

Detection of characteristic points of ECG using quadratic sp...

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International Conference on signals, Circuits and Systems, SCS

作者： Mohammad Niknazar Bijan Vosoughi Vahdat Seyed Reza Mousavi Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Biomedical Engineering Laboratory Sharif University of Technology Tehran Iran

ISBN: (纸本)9781424443970;9781424443987

This paper presents a method for ECG characteristic points detection based on Wavelet Transform (WT). Wavelet transform leads to more accurate results in analyzing nonstationary signals such as ECG. The selected wavelet is quadratic spline wavelet. Using quadratic spline mother wavelet, a method for detection of QRS complex and T and P waves is presented and evaluated with the help of MIT-BIH Arrhythmia database. Experimental results show excellent performance of the proposed method.

关键词： Electrocardiography Spline Wavelet transforms signal processing signal analysis Databases Band pass filters Matched filters Laboratories signal resolution

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Techniques for Automatic Magnetic Resonance image Classification

Techniques for Automatic Magnetic Resonance Image Classifica...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Hsian-Min Chen Shih-Yu Chen Jyh Wen Chai Clayton Chi-Chang Chen Chao-Cheng Wu Yen-Chieh Ouyang Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions.

关键词： Classification algorithms Training image segmentation Brain Skull Software algorithms Support vector machines

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Automated Volumetric Measurement of Brain Structures and White Matter Lesions in Alzheimer's Disease and Mild Cognitive Impairment

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Alzheimer's & Dementia 2010年第4期6卷 S22-S23页

作者： Huali Wang Frithjof Kruggel Daniel Chang Min-Ying Su Xin Yu Peking University Institute of Mental Health Beijing China Key Laboratory for Mental Health Ministry of Health (Peking University) Beijing China Signal and Image Processing Lab Department of Biomedical Engineering University of California Irvine CA USA Tu & Yuen Center for Functional Onco-Imaging University of California Irvine CA USA

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IC-P-050: Automated Volumetric Measurement of Brain Structures and White Matter Lesions in Alzheimer's Disease and Mild Cognitive Impairment

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Alzheimer's & Dementia 2010年第4S_PART_1期6卷

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Posture matching and elastic registration of a mouse atlas to surface topography range data

Posture matching and elastic registration of a mouse atlas t...

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IEEE International Symposium on biomedical Imaging

作者： A. A. Joshi A. J. Chaudhari Changqing Li D.W. Shattuck J. Dutta R. M. Leahy A. W. Toga Laboratory of Neuro Imaging University of California Los Angeles Los Angeles CA USA Department of Biomedical Engineering University of California Davis Davis CA USA Signal and Image Processing Institute University of Southern California Los Angeles CA USA

ISBN: (纸本)9781424439317

Estimation of internal mouse anatomy is required for quantitative bioluminescence or fluorescence tomography. However, only surface range data can be recovered from all-optical systems. These data are at times sparse or incomplete. We present a method for fitting an elastically deformable mouse atlas to surface topographic range data acquired by an optical system. In this method, we first match the postures of a deformable atlas and the range data of the mouse being imaged. This is achieved by aligning manually identified landmarks. We then minimize the asymmetric L 2 pseudo-distance between the surface of the deformable atlas and the surface topography range data. Once this registration is accomplished, the internal anatomy of the atlas is transformed to the coordinate system of the range data using elastic energy minimization. We evaluated our method by using it to register a digital mouse atlas to a surface model produced from a manually labeled CT mouse data set. Dice coefficents indicated excellent agreement in the brain and heart, with fair agreement in the kidneys and bladder. We also present example results produced using our method to align the digital mouse atlas to surface range data.

关键词： Mice Surface topography Surface fitting Anatomy Bioluminescence Fluorescence biomedical optical imaging Computed tomography Heart Bladder

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Fabrication of QCM sensor array and PDMS micro chamber for biosensor applications

Fabrication of QCM sensor array and PDMS micro chamber for b...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： K. Jaruwongrungsee T. Maturos K. Wong-ek M. Sangworasil C. Pintavirooj A. Wisitsora-at A. Tuantranont Biomedical Signal and Image Processing Laboratory Department of Electronics Engineering Faculty of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Nanoelectronics and MEMS Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Nanosceince and Technology Program Graduated College Chulalongkom University Bangkok Thailand

In this work, an array of quartz crystal microbalance (QCM) sensors is fabricated on a single quartz crystal substrate and attached with poly(dimethylsiloxane) (PDMS) micro chamber for using in flow-injection system. The Cr/Au electrode array is deposited on both side of quartz substrate by sputtering through electroplated microshadow mask. PDMS micro chamber was fabricated by mold casting technique with SU-8 mold. Fabricated PDMS micro chamber and QCM sensor array were attached together for using in flow-injection system. In our experiment, the QCM electrodes were coated with the carboxylic poly(vinyl chloride) (PVC-COOH) and then the carboxylic group was activated with carbodiimide hydrochloride (EDC) and N-hydroxylsuccinimide (NHS) for protein binding. The scanning electron micrograph shown the trapped protein on the modified sensing layer, it confirmed that our QCM sensor and PDMS micro chamber can be used as QCM biosensor array. This new QCM sensor array provide possibility of multiple detection in small amount of sample within a single quartz crystal substrate that avoid the error signal from the different properties of each sensor in an array.

关键词： Sensor arrays Biosensors Fabrication Sensor systems Electrodes Proteins Chromium Gold Sputtering Casting

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Model-based ECG fiducial points extraction using a modified extended Kalman filter structure

Model-based ECG fiducial points extraction using a modified ...

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International Symposium on Applied Sciences on biomedical and Communication Technologies, ISABEL

作者： Omid Sayadi Mohammad B. Shamsollahi Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran

This paper presents an efficient algorithm based on a nonlinear dynamical model for the precise extraction of the characteristic points of electrocardiogram (ECG), which facilitates the HRV analysis. Determining the precise position of the waveforms of an ECG signal is complicated due to the varying amplitudes of its waveforms, the ambiguous and changing form of the complex and morphological variations with unknown sources of drift. A model-based approach handles these complications; therefore a method based on the usage of this concept in an extended Kalman filter structure has been developed. The fiducial points are detected using both the parameters of Gaussian-functions of the model, and the state variable estimates. Several MIT-BIH ECG records are used for performance evaluation. Results show that the proposed method has an average sensitivity of 100% and a specificity of 99.93%. Simulation results illustrate that the method can contribute to and enhance the clinical ECG points extractions performance and exact tachogram representation.

关键词： Electrocardiography Hidden Markov models Timing Kalman filters Noise reduction State estimation Heart rate variability Heart beat Wavelet transforms Noise robustness

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