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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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Computer aided analysis of cognitive disorder in patients with Parkinsonism using machine learning method with multilevel ROI-based features

Computer aided analysis of cognitive disorder in patients wi...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Bo Peng Zhiyong Zhou Chen Geng Baotong Tong Zhe Zhou Tao Zhang Yakang Dai Changchun Institute of Optics Chinese Academy of Sciences Changchun China University of Chinese Academy of Sciences Beijing China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China

ISBN: (纸本)9781509037117

This paper proposes to use multilevel ROI-based features and machine learning method to improve the accuracy of qualitative recognition of mild cognitive disorder in parkinsonism. 77 Parkinson's patients and 32 normal controls with neuropsychological assessments and structural magnetic resonance images from the Parkinson's Progression Markers Initiative dataset are tested. Specifically, the BrainLab software is used to process images and measure volume of gray matter, thickness of the cortex, and surface area of the cortex at each region of interest (ROI). We utilize t-test, support vector machine (SVM), and minimum redundancy and maximum relevance (mRMR) methods conjunctively to select features and get the optimal features and the classifier. The experimental results reveal that our method with multilevel ROI-based features gives significant improvement of the classification performance compared with other methods using single-level ROI-based features (i.e., using only volume of gray matter, thickness of cortex, or surface area of cortex).

关键词： Feature extraction Support vector machines Parkinson's disease Dementia Learning systems Surface morphology

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Evaluation of Dosage Effect of Position Numbers in IMRT for Breast Cancer

Evaluation of Dosage Effect of Position Numbers in IMRT for ...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Yuan Zhao Wei Peng School of Biomedical Engineering Hubei University of Science and Technology Xianning China

ISBN: (纸本)9781509037117

Objective To evaluate the dosage effect of position numbers in intensity-modulated radiotherapy plan (IMRT) for breast cancer. Method A 56 year old patient with breast cancer was selected and treated by IMRT in this study. According to the images of the position verification, the target dosage and organ at risk (OAR) were calculated and verified in detail. Results When the position number was 5, the doses of the target and OARs were CTV 48.8Gy, PTV 48.2Gy, spinal cord 27.5Gy, lung 5.5Gy with V_(20)<11.5%, heart 16.0Gy with V_(10)<33.7%, femoral head 38.2Gy. Although all the doses met the prescription of radiotherapeutic physicist, however, the doses of OAR closed to the dosage limitation. In order the decrease the doses of OAR, the number of position verification increased to 7. Therefore the doses of OAR decreased significantly. Conclusion The positioning error can be decreased by increasing the number of position verification.

关键词： organ at risk breast cancer intensity-modulated radiation therapy position number

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Super-resolution of 3D MR images and its application to brain segmentation

Super-resolution of 3D MR images and its application to brai...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Yutaro Iwamoto Xian-Hua Han Akihiko Shiino Yen-Wei Chen Graduate School of Information Science and Engineering Ritsumeikan University National Institute of Advanced Industrial Science and Technology Biomedical MR Science Center Shiga University of Medical Science College of Information Science and Engineering Ritsumeikan University

ISBN: (纸本)9781509037117

This paper presents super resolution (SR) of 3D MR images which is effective for brain segmentation as practical application. Brain segmentation is one of important tasks to analyze brain morphometry. An accurate brain segmentation helps improve accuracy of post-processing. The segmentation is affected by the resolution of 3D MR images. In particular, the resolution in slice-select direction is much lower than the in-plane direction in some type of 3D MR images (e.g. T2-weighted images and proton density images). Therefore, we apply a learning-based SR method using sparse representation and self-similarity for generating high-resolution (HR) in slice-select direction to be same resolution in in-plane direction. With the visualized evaluation, it can be seen that the segmentation accuracy in SR results are improved by reducing partial volume effect. With the quantitative evaluation, one can confirm that the DICE value of SR results are higher than conventional interpolation method.

关键词： segmentation super-resolution (SR) sparse representation self-similarity

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A novel method for adjusting Digital predistortion with Modulation Error Ratio in OFDM System

A novel method for adjusting Digital predistortion with Modu...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Qun Chen Yang Song Feng Hu Chaoshi Cai School of Information Engineering Communication University of China Beijing China

ISBN: (纸本)9781509037117

Power amplifiers (PAs) are important components in OFDM systems which can efficiently uses the spectrum but very sensitive to constellation error. The nonlinearity of PAs causes in-band distortion and spectral regrowth, thereby increasing the constellation error. To overcome the PA's nonlinearity, Digital predistortion is a highly cost-effective way to linearize PAs. When modeling of digital predistorter, there are many coefficients that determining the predistorter's characteristics which are affecting the effect of predistortion. Different values of the coefficients lead to a large gap in the effect of predistortion, thus constructing the predistorter with appropriate coefficients is complex and necessary. Many research when estimating the coefficients by minimizing mean square error (MMSE) of the signal amplified by the amplifier. But the MSE can't indicate the constellation error rate, furthermore, OFDM rely on the constellation map to transfer message, so that the coefficients corresponding to MMSE method may not the most appropriate. In this paper we propose a novel method to estimate the predistorter coefficients by maximizing the Modulation Error Ration (MER) which can directly indicate the degree of constellation error, and we introduce the quasi-Newton method which can help us find the optimum coefficient to maximize MER. The proposed predistorter can better reduce the constellation error.

关键词： Predistortion Analytical models Nonlinear distortion Peak to average power ratio Modulation

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Compressive sensing aided the sequential extended Kalman filter tracker for pulse Doppler radar

Compressive sensing aided the sequential extended Kalman fil...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Xuejun Wang Jinping Sun Xiuwei Yang School of Electronic and Information Engineering Beihang University Beijing China Automation institute of Shandong Academy of Sciences Jinan China

ISBN: (纸本)9781509037117

Since target tracking using pulse Doppler (PD) radar plays a significant role in military applications and the traditional tracking methods are not perfect enough to complete the tracking filter process, compressive sensing (CS) is applied for improving the tracking precision. In this paper, CS aided sequential extended Kalman filter (SEKF) is proposed to track moving targets using pulse Doppler (PD) radar. We use the sparsity of delay-Doppler plane to set up a sparse signal model in each pulse interval, and get Doppler measurements through the reconstruction algorithm. Then SEKF is used to make filter update so as to attain the high-precision state estimation. In the process of filter, we use SEKF to reduce the nonlinearity between Doppler measurements and the target motion state. This method can not only take advantages of CS, but also decrease the nonlinear error through adding the pseudo, which can improve the tracking accuracy of PD radar more significantly. Numerical simulations show that our proposed algorithm is validated to enhance the tracking performance compared to the traditional SEKF method and the CS based tracking method. In addition, parameters used in this paper are more practical and of great significance and value.

关键词： Target tracking Radar tracking Doppler measurement Doppler radar Kalman filters

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Recognition of partially occluded face by error detection with logarithmic operator and KPCA

Recognition of partially occluded face by error detection wi...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Xiaolin Chen Shunfang Wang Xiaoli Ruan School of Information Science and Engineering Yunnan University Kunming China

ISBN: (纸本)9781509037117

Occluded images often affected the recognition rates in face recognition, thus the occlusion should be checked out and given a little weighting coefficient so as to weaken its impact on the recognition rate as much as possible. The traditional algorithms often use the reconstruction error operator based on principal component analysis (PCA) to estimate the weight for occluded face, which often need iterative computation and consequently high time complexity. Moreover there are many difficulties for noise problems and threshold selection in iteration. In order to overcome the shortcoming, this paper suggested a new research method of face recognition on the basis of error detection and kernel principal component analysis (KPCA) algorithm under partial occlusion. First, face images were divided into several regions. Then, the weight of each area is calculated by a new logarithmic transformed Gaussian error operator instead of the traditional error operator. Finally, each region's features are extracted by KPCA, which is a nonlinear transformation and mapping method. All features are fused with their weights to realize the final classification and recognition. The experimental results with the AR facial database indicated that the new algorithm was of great robustness and validity.

关键词： Face Face recognition Principal component analysis image reconstruction Feature extraction Kernel Algorithm design and analysis

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Low-Rank Plus Sparse Reconstruction Using Dictionary Learning For 3D-MRI

Low-Rank Plus Sparse Reconstruction Using Dictionary Learnin...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Wenxiong Zhong Dongxiao Li Lianghao Wang Ming Zhang College of Information Science and Electronic Engineering Zhejiang University

ISBN: (纸本)9781509037117

This work proposes a low-rank plus sparse model using dictionary learning for 3D-MRI reconstruction from down-sampling k-space data. The scheme decomposes the dynamic image signal into two parts: low-rank part L and sparse part S and then, constructing it as a constrained optimization problem. In the optimization process, a nonconvex penalty function is used to optimize the low rank part L. The sparse part S is expressed by a over-complete dictionary using blind compressed sensing and we formulate the sparsity of coffecient matrix using l_1 norm. To avoid the ill-posed of the problem, the Frobenius norm is used in dictionary. We adopt an alternate optimization algorithm to solve the problem, which cycles through the minimization of five subproblems. Finally, we prove the effectiveness of proposed method in two cardiac cine data sets. Experimental results were compared with exsiting L+S, L&S and BCS schemes, which demonstrate that the proposed method behaves better in removal of artifacts and maintaining the image details.

关键词： undersampled reconstruction Blind compressed sensing(BCS) dynamic magnetic resonance imaging(dMRI) low-rank plus sparse dictionary learning

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Functional Research on Digital Cognitive Ethology of Transcranial Direct Current Stimulation

Functional Research on Digital Cognitive Ethology of Transcr...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Heng Wang Xin Wang Jianan Wu Xianwen Zhang Liyan Zhang Zihan Zhuo Jingtian Tang Key Laboratory of Particle Technology and Radiation Imaging of Ministry of Education Institute of Medical Physics and Engineering Department of Engineering Physics Tsinghua University Electrical engineering and its automation School of Technology Beijing Forestry University National Computer Network Emergency Response Technical Team/Coordination Center of China

ISBN: (纸本)9781509037117

The paper, based on the current researches on the regulation and control of digital cognitive competence by transcranial direct current stimulation (TDCS), adopts stimulation device developed by Tsinghua University to launch the analysis experiment of cognitive behaviors aiming at the key problem of the application effects of TDCS, compares the influences of stimulating electrode parameters on digital cognitive behaviors and conducts the initial validation of TDCS effects and simulation results. The experiment finds that the positive TDCS of dorsal lateral prefrontal cortex (DLPFC) can improve complex mental calculation ability whose mechanism is related to electrical stimulation enhancing working memory capacity. The research can help to understand the relation between mathematical calculation, relevant brain areas and stimulation which provides reference for the development of effective intervention methods of digital cognitive disorders.

关键词： numerical calculation experiment formatting TDCS Digital Cognitive cognitive ethology

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An Automatic Red Blood Cell Counting Method Based on Spectral images

An Automatic Red Blood Cell Counting Method Based on Spectra...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Jingyi Lou Mei Zhou Qingli Li Chen Yuan Hongying Liu Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University

ISBN: (纸本)9781509037117

Blood cell analysis, including blood cell counting, is the key point for modern pathological study as well as medical diagnosis. Taking into account both resources and environment of the medical research, analyzing blood cells under the microscope, instead of dedicated blood cell analyzer, provides a more intuitive and convenient way for research uses. This paper aims to provide a method to count red blood cells (RBCs) automatically by analyzing blood cell images collected from a microscopic hyperspectral imaging system. The classification algorithms - spectral angle mappings (SAMs) and support vector machines (SVMs) are used to segment blood cell image. In order to identify RBCs in the image, a standard RBC model has been built to match RBCs in the segmentation results based on SAM classification algorithm. RBC counting results are therefore obtained from the identification and the counting accuracy reaches about 93%. For the sake of higher precision, an improved algorithm, using segmentation results based on SVM classification algorithm to screen the previous matching results, is proposed and the counting accuracy increases to about 98% after applying the improved algorithm.

关键词： RBC counting hyperspectral image blood cell segmentation red blood cells (RBCs)

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Synchrony analysis using different cross-entropy measures of the electroencephalograph activity in Alzheimer's disease

Synchrony analysis using different cross-entropy measures of...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Ruofan Wang Dianwei Li Jiang Wang Lihui Cai LianShuan Shi Dept. Information Technology Engineering Tianjin University of Technology and Education Tianjin China Dept. Electrical & Automation Engineering Tianjin University Tianjin China

ISBN: (纸本)9781509037117

In this paper, in order to explore underlying the interaction mechanisms between brain regions, the cross entropy measures: cross sample entropy (C-SampEn) and cross fuzzy entropy (C-FuzzyEn) were used to measure the brain synchrony through analyzing the background electroencephalograph (EEG) signals in Alzheimer's disease (AD) patients. It was demonstrated that the values of both the two features in AD were lower than that of the normal controls, particularly in the alpha band, indicating that the synchrony strength and the information transferring efficiency between the corresponding brain regions were decreased in AD. Moreover, classification results showed that the two cross entropy features could classify AD Group and Control Group correctly. However, the C-FuzzyEn performed better, reflected in the higher classification accuracy. Thus, C-FuzzyEn revealed the abnormalities of brain activity for AD by evaluating the synchrony from EEG series, and it seemed to be an important auxiliary clinical method to diagnose AD early.

关键词： Entropy Electroencephalography Electrodes Alzheimer's disease Statistical analysis Feature extraction

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