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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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Automatic method for lung segmentation with juxta-pleural nodules from thoracic CT based on border separation and correction

Automatic method for lung segmentation with juxta-pleural no...

引用

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

作者： Yanxia Sun Jinke Wang Department of Software Engineering Harbin University of Science and Technology Rongcheng China

ISBN: (纸本)9781509037117

In this paper, a fully automatic method was proposed for lung segmentation with juxta-pleural nodules from CT. The approach consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest boundary is extracted through image aligning, morphology operation and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 20 volumes of chest scans, with volume difference (VD) 11.15 ± 69.63 cm 3 , volume overlap error (VOE) 3.5057 ± 1.3719%, average surface distance (ASD) 0.7917 ± 0.2741 mm, root mean square distance (RMSD) 1.6957 ± 0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430 ± 8.1743 mm, and average time-cost 2 second per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.

关键词： Lungs image segmentation Computed tomography Smoothing methods image reconstruction Morphology Noise reduction

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Application of a modified LSQR method for CT imaging reconstruction with low doses to patient

Application of a modified LSQR method for CT imaging reconst...

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

作者： Liubov Flores Vicente Vidal Estíbaliz Parcero Gumersindo Verdú Departamento de Ciencias Exactas Universidad Peruana Cayetano Heredia Lima Perú Departamento de Sistemas Informáticos y Computación Universitat Politècnica de València Valencia Spain Departamento de Ingeniería Qumica y Nuclear Universitat Politècnica de Val ència Valencia Spain

ISBN: (纸本)9781509037117

Iterative methods have become a hot topic of research in computed tomography (CT) imaging because of their capacity to resolve the reconstruction problem from a limited number of projections. This allows the possibility of reducing radiation exposure on patients during the data acquisition. However, the complexity of the data still makes the reconstruction process time-consuming, especially for 3D cases. The reconstruction time and the high radiation dose imposed on patients are the two major drawbacks in CT. To resolve them effectively we adapted the method for sparse linear equations and sparse least squares (LSQR) with soft threshold filtering (STF) and the fast iterative shrinkage-thresholding algorithm (FISTA) to computed tomography reconstruction. While STF technique helps to preserve the edge data structure of the object, FISTA accelerates the convergence of the solution and improves the image quality further. The efficiency and feasibility of the proposed method are demonstrated using numerical calculations.

关键词： Computed tomography Manganese Acceleration biomedical imaging image coding

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Online dynamic magnetic resonance imaging based on radial sampling and GPU acceleration

Online dynamic magnetic resonance imaging based on radial sa...

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

作者： Xu Wang Zhaoyang Jin Institute of Information and Control Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781509037117

In many fields of MRI (Magnetic Resonance Imaging), especially in DMRI (Dynamic MRI) domain, long acquisition time often limits its clinical applications. The new online MRI technique can obviously accelerate the imaging time for DMRI based on under-sampling acquisition and sparsity performance between neighbor frames. The data acquisition of previous online MRI schemes was based on the Cartesian coordinate. Non-Cartesian sampling, such as radial sampling, can obtain higher data acquisition speed than conventional Cartesian sampling. In order to further accelerate the imaging speed for online MRI, radial under-sampling instead of Cartesian under-sampling, was proposed to shorten the data acquisition time for DMRI. However the reconstruction of non-Cartesian data is complicated and time consumed, so GPU (Graphics processing Unit) was used to shorten the reconstruction time. In this primary study, the full sampled cardiac dataset was used as a reference dataset, radial under-sampling with 25% and 10% k-space coverage was simulated for the online imaging scheme. The results show that radial under-sampling scheme obtained higher image quality than that of Cartesian acquisition scheme, with its imaging time still satisfying online reconstruction after applying GPU acceleration.

关键词： image reconstruction Magnetic resonance imaging Graphics processing units Trajectory Acceleration Data acquisition

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A novel rolling bearing fault diagnosis method

A novel rolling bearing fault diagnosis method

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

作者： Fan Zhang Tao Zhang Hang Yu Department of Software Engineering Beijing University of Technology Beijing China School of computer engineering and science Shanghai University Beijing China

ISBN: (纸本)9781509037117

In order to solve the problem of traditional methods having low accuracy in recognizing rolling bearings' faults and to reduce time in building a training model, this paper puts forward a method of recognizing faults based on wavelet packet transformation and PCA-PSO-MSVM. First of all, we extracted the energy values after all kinds of faulty signals have been transformed through wavelet packet, and combined the index of the signals' time-domain characteristics to form an eigenvalue matrix to be used as the characteristics of SVM sample training. Then by using PCA (principal component analysis) method, we eliminated the redundant characteristics to increase the efficiency of the model structure. Afterwards, through MSVM algorithm and one-to-one strategy, we carried out final multi-classification recognition of the mixed faults of rolling bearings, with PSO (particle swarm optimization) algorithm also adopted for optimizing SVM parameters to improve the accuracy of classifications. The experiment adopted the data of rolling bearings provided by Case Western Reserve University. As shown in the experimental results, this method has high accuracy and efficiency.

关键词： Rolling bearings Support vector machines Training Wavelet packets Classification algorithms Indexes Feature extraction

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Feature Extraction for Fault Diagnosis Utilizing Supervised Nonnegative Matrix Factorization Combined Statistical Model

Feature Extraction for Fault Diagnosis Utilizing Supervised ...

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

作者： Yuanming Chen Maolin Li Lin Liang Guanghua Xu Huizhong Gao School of Mechanical Engineering Xi'an Jiaotong University China Engineering Workshop Xi'an Jiaotong University China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi'an Jiaotong University China

ISBN: (纸本)9781509037117

A new method for separating the feature automatically with supervised nonnegative matrix factorization (NMF) is proposed for fault diagnosis. Because of the shortage of lacking prior knowledge in existed NMF, a supervised NMF combined statistical model for fault diagnosis is proposed. The basis matrix achieved in training stage is treated as the sources' features. Besides, Gaussian Mixture Model is introduced to estimate the distributions of the base vectors and then keep them as the prior knowledge. The rotation machinery with faults was used to evaluate the performance of the proposed method. The results show that the proposed method has a good source separation capability. Its performance is better than NMF.

关键词： Supervised learning Nonnegative matrix factorization Fault diagnosis Source separation

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A Peak to Average Power Ratio Reduction Scheme in ACO-OFDM System Employing Constant Amplitude Zero Auto Correlation and Clipping

A Peak to Average Power Ratio Reduction Scheme in ACO-OFDM S...

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

作者： Yongxia Li Yufang Zhou Enshun Kang Tianshu Zhao School of Computer Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China Overhaul Department Cabin Refurbishment Shop CSN Shenyang Aircraft Maintenance & Overhaul Base China

ISBN: (纸本)9781509037117

In classical asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) system, we first introduce constant amplitude zero auto correlation (CAZAC) equalization to reduce its high peak to average power ratio (PAPR). To further improve the performance of PAPR reduction, we combine CAZAC with the existing clipping scheme, and we call it CAZAC-clipping scheme. In the frequency domain, CAZAC changes the phase distribution, and reduces the probability of in-phase combination. In the time domain, clipping clips all the values which larger than the predetermined peak threshold. The simulation results demonstrate that both the PAPR reduction and the bit error rate (BER) performance of our proposed scheme show well. Moreover, the computation complexity is low.

关键词： ACO-OFDM PAPR CAZAC clipping

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An improved feature fusion algorithm for image retrieval

An improved feature fusion algorithm for image retrieval

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

作者： Shilin Che Dongfang Chen Xiaofeng Wang Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology

ISBN: (纸本)9781509037117

Single feature extract method for image retrieval usually cannot get good results and it is hard to find a stable fusion method when across image datasets because the image descriptors usually describe image from single aspect. For this pragmatic issue, an improved FCTH-BoVW feature fusion algorithm called robust FCTH-BoVW is proposed in this paper. Traditional feature fusion methods almost use liner weight method, but this method will be easily affected by the weights, and not easy to find the optimal weights. In this paper, FCTH feature is used as the global feature and BoVW feature as the local feature, and use k-reciprocal nearest neighbors method to fuse them. FCTH is an efficient descriptor and contains both color and texture information, in order to emphasize the center area as main region of interest, an annular weight method is proposed to improve the global feature. A soft assignment method is adopted to quantize the visual words for the improvement of the local feature. This method enhances the description ability of both global and local features. We find those improvements are quite practical, the results show that this fused method gets a higher retrieval precision, and we also find it is a stable image retrieval method for varieties of image datasets.

关键词： Feature extraction Histograms image color analysis Visualization Fuses image retrieval image segmentation

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Distance selection for image super-resolution by dictionary optimization

Distance selection for image super-resolution by dictionary ...

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

作者： Jianguang Zhang Yongxia Li Jing Li Baoying Liu Department of Mathematics and Computer Science Heng Shui University Heng Shui China Office of Educational Administration Heng Shui University Heng Shui China College of Electronic Information and Automation Tianjin University of Science&Technology Tian Jin China Department of Political Science and Law Heng Shui University Heng Shui China

ISBN: (纸本)9781509037117

The quality of Learning-based super-resolution (SR) largely depends on whether the dictionary includes rich details which are strongly similar to the input image. In this paper, for each input image, we optimize the dictionary by selecting the most similar images based on the SIFT feature. A rich set of candidate distance metrics, such as Median distance, Euclidean distance, Harmonic distance and Geometric distance, are explored to match the SIFT feature, which can provide more accurate matching result than only using Euclidean distance. The optimal distance of each target image can be selected by comparing the four super-resolution images, which are recovered by using each distance metric. According the optimal distance metric, all target images are divided into three groups. We propose a novel bag-of-words (BOW) model for quantize the input image and target images, and then select the optimal distance of input image by finding the most similar target image. Experimental results demonstrate that our method can obtain the high hit rate and the select distance is more adaptive for input image than the traditional Euclidean distance.

关键词： image resolution Euclidean distance Dictionaries Training signal resolution Harmonic analysis

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A multi-sensor based pre-impact fall detection system with a hierarchical classifier

A multi-sensor based pre-impact fall detection system with a...

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

作者： Yiwen Su Daliang Liu Yingfeng Wu School of Instrument Science and Electronic Engineering Beihang University Beijing China Capital Aerospace Machinery Company Beijing China School of Instrument Science and Opto-Electronic Engineering Beihang University Beijing China

ISBN: (纸本)9781509037117

Fall is a major threat to elders' health. The goal of our study is to establish a pre-impact fall detection system to reduce the harm of falls. For the problem that single-sensor based system can't achieve high accuracy, we propose a multi-sensor based system, which can fuse the data from waist and thigh. Collected data are transferred to a computer or a cellphone using wireless Bluetooth technique. A discrimination analysis based pre-impact fall detection model is developed. Human activities can be classified into three categories (non-fall, backward fall and forward fall) using a hierarchical classifier. In order to improve the classification accuracy, optimal discriminant features are selected for each layer of classifier. Then, experiments are conducted and the results show that our method can both achieve high sensitivity and specificity as well as long lead time.

关键词： Feature extraction Legged locomotion Thigh Angular velocity Correlation coefficient Lead Sensitivity

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A robust point detection algorithm based on wavelet transform for visual tracking

A robust point detection algorithm based on wavelet transfor...

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

作者： Rongfeng Zhang Huanhou Xiao Ting Deng Wei Qiu Jinglun Shi Guangzhou College of South China University of Technology Guangzhou P. R. China School of Electronic and Information Engineering South China University of Technology Guangzhou P. R. China Information Network Engineering and Research Center South China University of Technology Guangzhou P. R. China

ISBN: (纸本)9781509037117

Visual tracking is one of the hot research topics in computer vision in recent years. It has been widely used in many vision applications, such as traffic surveillance, anti-terrorism. However, there are still challenges for visual tracking, like illumination change, object occlusion, appearance deformation, etc. This paper proposes a robust point detection algorithm based on wavelet transform for visual tracking. First, the input image patch that includes the tracking object is decomposed by wavelet transform with several levels and the wavelet coefficients are obtained. The wavelet coefficients are then analyzed and the points that hold the local maximal wavelet coefficients are determined as the robust points for tracking. Finally, the proposed method is integrated to the Tracking Learning Detection (TLD) framework, which not only improves the tracking precision, but also reduces the false detection. Experimental results showed that the new algorithm outperformed the TLD method with respect to the precision, recall, and f-measure.

关键词： Target tracking Robustness Wavelet coefficients image resolution Lighting

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