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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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Motion-Blind Blur Removal for CT images with Wasserstein Generative Adversarial Networks 11

Motion-Blind Blur Removal for CT Images with Wasserstein Gen...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Lyu, Yilin Jiang, Wei Lin, Yanjun Voros, Laszlo Zhang, Miao Mueller, Boris Mychalczak, Borys Song, Yulin Columbia Univ Dept Stat Arts Stat New York NY USA Yantai Yuhuangding Hosp Dept Radiat Oncol Yantai Peoples R China Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10021 USA Mem Sloan Kettering Canc Ctr Dept Radiat Oncol 1275 York Ave New York NY 10021 USA Mem Sloan Kettering Canc Ctr Dept Med Phys Montvale NJ 07645 USA

ISBN: (纸本)9781538676042

Advanced deblurring techniques for computed tomography (CT) images are necessary and crucial to the improvement of accuracy of patient diagnosis in radiology and patient setup and treatment response assessment in radiation oncology. Currently, medical image deblurring is a challenging technical problem due to the unpredictability of patient motion. This paper introduces a new method of computed tomography image deblurring based on Conditional Generative Adversarial Networks (CGAN) that have been broadly implemented in computer vision research. A Wasserstein Generative Adversarial Network (WGAN) with adversarial loss and l1 perceptual loss was proposed and trained by a blur-sharp image pair dataset created in-house and evaluated by Peak signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). These experiments showed the effectiveness of the approach, which outperforms other competing deblurring techniques both quantitatively and qualitatively.

关键词： Computed tomography Training Generators image restoration Gallium nitride PSNR

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Non-Local Spatiaotemporal Two-Stream Network for Video Action Recognition

Non-Local Spatiaotemporal Two-Stream Network for Video Actio...

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

作者： Jie Zheng Yu Zhu College of Information Science and Engineering East China University of Science and Technology Shanghai China

Current video action recognition methods mainly used repeated convolution and recurrent operation to obtain the spatial and temporal information. But these operations only process local neighborhood in space and time and are not sensitive to global information, which may lead to wrong prediction for some large-space and long-time span actions. So in this paper, we proposed a non-local 2D spatiotemporal two-stream network, and incorporate the non-local module to I3D spatiotemporal two-stream network. This module calculates the embedded Gaussian response at a position as a weighted sum of the features at all positions in the input feature maps. The obtained non-local information enhances the global feature in both space and time effectively. In the proposed network, we first use several low-level layers to obtain low-level feature maps, then passes the non-local module to obtain non-local information. Finally, using the subsequent layers to get prediction result. The performances of the proposed 2D model and the improved I3D model were evaluated on UCF-101 dataset and HMDB-51 dataset, and achieve 95.27% and 69.61% recognition accuracy respectively.

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Preliminary Study of Optimization of Mri-compatible Ultrasonic Transducer Matching Layer Composites with Comsol

Preliminary Study of Optimization of Mri-compatible Ultrason...

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

作者： Xinping Ren Wenjie Liu Zhiqiang Su Hao Wu Shan Qiao Huaxin Lu Guofeng Shen School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Shende Medical Technology Co. Ltd. Shanghai China

Phase controlled high intensity focused ultrasound (p-hifu) is electronic phase controlled *** adjusting the excitation voltage phase and amplitude of each array element, a variety of sound field modes, such as single-focus movement and multi-focus, can be generated. Magnetic resonance imaging (MRI) can reflect tissue details in nondestructive conditions and measure the temperature of tissue in vivo by analyzing the proton resonance frequency signals. The combination of p-hifu technology and MRI technology improves the accuracy and safety of treatment, but the combination of the two brings about non-negligible electromagnetic interference problems. Most EMC problems can be solved by electromagnetic shielding. Based on previous studies, this paper intends to study a novel electromagnetic shielding material Cu-Epoxy for ultrasonic *** biggest innovation of this material is its dual functions- electromagnetic shielding and ultrasonic matching *** conduct preliminary simulation with Comsol software to verify its feasibility. Through the simulation of electromagnetic field and piezoelectric effect, we find that this material is feasible in theoretical design. The biggest advantage of this simulation model is that it can be used for theoretical design of composite materials and explore the optimization of their performance. In further research, we can develop the model from the simplest two-dimensional axisymmetric model to a three-dimensional model, making it closer to the real situation.

关键词： Composite materials Acoustics Copper Ultrasonic imaging Phased arrays Powders Electromagnetic shielding

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Generating Conditions of Focused Virtual Channels Under Simultaneous Electrical Stimulation of Epiretinal Prostheses: A Modeling Study

Generating Conditions of Focused Virtual Channels Under Simu...

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

作者： Tong Li Shirong Qiu Xiaoyu Song Liming Li School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

An epiretinal prosthesis is a promising approach that help patients suffering from retinal degeneration restore partial visual function by activating the surviving retinal ganglion cells. Although some achievements have been made in clinical and commercial practice, a serious problem is that the perceptual resolution of existing prostheses is still low and unsatisfactory. Virtual channels caused by simultaneous dual electrode stimulation (i.e. current steering stimulation strategy) can effectively improve the spatial perceptual resolution of neural prostheses. However, the electric fields produced by current steering stimulation tend to become broader than the single electrode stimulation. To research how to generate the ideal virtual channels, this study focused on investigating the generation conditions of converged electric fields by current steering under epiretinal stimulation. In this study, we calculated theoretically the electrical field profiles in the epiretinal space under different parameters of current steering stimulation, and investigated the relationship among the radius of stimulating electrode, the edge to edge distance between electrodes (EED) and the electrode-retina distance (ERD) that can generate converged single-peaked electric fields. We also found that the convergence of electric fields could be influenced by the ratio of current delivered to the dual stimulating electrodes (defined as stimulation coefficient α), and further evaluated the conditions under which the focused single-peaked electric field could be produced at different α, This study will benefit to the design of appropriate electrode parameters to generate converged virtual channels in epiretinal prostheses.

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A Pedestrian detection method Based on YOLOv3 model and image enhanced by Retinex 11

A Pedestrian detection method Based on YOLOv3 model and Imag...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Qu, Hongquan Yuan, Tongyang Sheng, Zhiyong Zhang, Yuan North China Univ Technol Beijing Peoples R China

ISBN: (纸本)9781538676042

Pedestrian detection is a basic technology in the field of intelligent traffic video surveillance. It is also help for the optimization design of rail transport. It is known that the deep learning technology can achieve considerable performance on pedestrian detection. However, this kind of methods demand a large number of high-quality samples. In addition, the quality of data sample in the subway station is usually sensitive to the background environment, such as variant illumination or pedestrian density, which can significantly affect performance of the deep neural network. To solve this problem, this paper adopts an image enhancement policy based on the Retinex theory to preprocess training samples to reduce the influence of light changes. Firstly, we use the image enhancement method to enhance the contrast of the image and highlight the color of the object itself. Next, we put the initial sample into darknet frame with YOLOv3 to train the detection model 1 and put the enhanced sample into the YOLOv3 to train the detection model 2. Finally, we tested these two models with 200 pedestrian pictures of four different scenarios. The experimental results show that the model trained by Retinex image enhancement has a more accurate detection rate of 94% compared with the model without the enhancement sample trained.

关键词： Retinex image enhancement YOLOv3 pedestrain detection

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The Vascular Calcification image Segmentation Based on CV Model 11

The Vascular Calcification Image Segmentation Based on CV Mo...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Jiang, Tiechao Ji, Xiaoqiang Jilin Univ China Japan Union Hosp Changchun Jilin Peoples R China Changchun Univ Sci & Technol Sch Life Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781538676042

In order to accurately segment the calcification of the vascular in medical images, a level set segmentation method based on CV model is designed. Firstly, the image preprocessing is carried out, which including that the median filter is used to remove the image noise, and the histogram equalization is used to enhance the image contrast. Next, according to the actual need we segment the region of interest including the vascular and calcification? And then, the level set segmentation method based on the CV model is adopted to segment the calcified points which adhere to the wall of the vascular. Finally, the morphological operation is used to eliminate the small noise and hole. A large number of clinical vascular calcification images were tested, and the experimental results clearly indicated that the level set segmentation method based on CV model can effectively segment the calcification, and detected the location, size, shape, etc... At the same time, the contrast experiment was designed, and the CV model was compared with OTSU threshold segmentation and hill climbing method. The results showed that the CV model is more accurate, the edge is smoother and clearer. The CV model segmentation algorithm is accurate and effective for detecting vascular calcification, and is convenient for further measurement of the calcification, that can provide effective evidence for clinical diagnosis.

关键词： vascular calcification image segmentation CV Model pretreatment

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An Improvement in image Registration with SIFT Features and Logistic Regression 11

An Improvement in Image Registration with SIFT Features and ...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Jia, Yongxing Sun, Weihao Rong, Chuanzhen Zhu, Ying Yang, Yu Army Engn Univ PLA Coll Commun Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538676042

image registration is a research hotspot in computer vision. Depending on the overlapping region between adjacent images, this technology can complete the task of matching and superposition of multiple images. In reality, getting highly accurate performance in complex motion states and meeting real-time requirement for high-resolution images simultaneously is still a challenge. It is observed that overlapping region occupies only 30% to 50% of adjacent images. Meanwhile, matching points are centralized in the overlapping region. Thus, detecting overlapping region can contribute to reducing time and improving the accuracy of image registration. In this paper, basing on labelled feature points, an approach for image registration using logistic regression is proposed. Firstly, fewer Scale-Invariant Feature Transform (SIFT) points are detected and labelled in different values to differentiate matching points and mismatching points. Then, regarding the labelled points as data in the training set, logistic regression assigns a predicted value to predict the category of each point. Finally, by reducing the total difference between the predicted values and the labelled values, corresponding parameters to describe the overlapping region are determined. Hence, the overlapping region is detected. Several sets of images are used to testify the validity of the proposed work. Experimental results show that: integrating with the invariant property of the feature points, this approach is robust and accurate to be applied to splice images in complex motion states.

关键词： image registration overlapping region sift feature logistic regression

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Acoustic Detection of ArterioVenous Access Stenosis Based on MUSIC Power Spectral Features

Acoustic Detection of ArterioVenous Access Stenosis Based on...

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

作者： Jinhai Zhou Jingping Tong Yang Chang Shiyi Zhou Yichuan Wang Hua Li Yibiao Huang Cheng Zhu Xiangfei Wu College of Information Science and Electronic Engineering Zhejiang University Hangzhou People's Republic of China Sir Run Run Shaw Hospital School of Medicine Zhejiang University Hangzhou People's Republic of China Hangzhou Magent Intelligent Technology Co. Ltd Hangzhou People's Republic of China

Arterio Venous Vascular Access (AVA) stenosis is a common complication in hemodialysis patients. Clinically, AVA stenosis happens when the cross-sectional area is reduced to less than 50% of the normal area. In order to highlight the correlation between the power spectral features of AVA Phonoangiography signals (PCG) and degree of stenosis (DOS), In-vitro BioPhysical Simulation Model (BPSM) is used to control individual conditions. Previous studies have pointed out that the features of PCG can be used to detect stenosis, but there were differences in the specific frequency band range. In this study, a method for extracting the power spectral features of PCG based on MUltiple signal Classification power spectrum estimation algorithm (MUSIC) is proposed. This method has a high resolution for the high-frequency low-energy sound caused by stenosis. Using the proposed method, a strong correlation is found between the frequency peak near 820 Hz (820 ±70 Hz) and the AVA stenosis. Based on the above feature extraction method, a support vector machine (SVM) classification model is trained on data obtained on the BPSM. Finally, using MUSIC features extraction model and SVM classification model, the correct classification rate on BPSM data is 96.4%, and the SVM model is validated on 19 clinical measured data, the accuracy is 84.2%.

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Multi-Level Classification of Arteriovenous Graft Degree of Stenosis Based on SSA + Welch Multi-Position Sequence Feature Extraction

Multi-Level Classification of Arteriovenous Graft Degree of ...

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

作者： Jinhai Zhou Yang Chang JingPing Tong Shiyi Zhou YiChuan Wang Hua Li Yibiao Huang Cheng Zhu Xiangfei Wu College of Information Science and Electronic Engineering Zhejiang University Hangzhou People's Republic of China Sir Run Run Shaw Hospital School of Medicine Zhejiang University Hangzhou People's Republic of China Hangzhou Magent Intelligent Technology Co. Ltd Hangzhou People's Republic of China

This study proposes a phonoangiography (PCG) signal processing method to help non-invasively diagnose Arteriovenous Graft (AVG) stenosis in patients with End Stage Renal Disease (ESRD). Pre-existing binary- classification studies for Degrees Of Stenosis (DOS) indicated that the presence of high frequency acoustic signals characterizes stenosis AVG, but the specific range they pointed out vary from each other, including 300Hz to 600Hz, 400Hz or more, 700Hz. In order to obtain the true relationship between the frequency characteristics of the acoustic signal and DOS of AVG, our team proposed a multi-position sequence feature (MPSF) extraction method, the judgement of DOS has also been expanded from binary-classification to multi-level classification. This method uses a physical model built by our team to simulate normal AVG and AVG with different DOS, we selected upstream 7cm and downstream 7cm of the stenosis section as two measurement positions to collect AVG acoustic signal. The components of the AVG acoustic signal below 200 Hz are filtered using singular spectrum analysis (SSA), and the remaining part is subjected to power spectrum estimation using the Welch method. The frequency spectrum energy obtained from the two measurement positions is combined into MPSF to be trained in the LSTM neural network, thereby realizing multi-level classification of different DOS. Tests on AVG physical models and clinical patient data sets show that, compared to previous studies, this method has the better effect on diagnosis of DOS.

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Comparing Performance of Iterative and Non-Iterative Classifiers for 2-Lead ECGs on Multi-Feature Schemes

Comparing Performance of Iterative and Non-Iterative Classif...

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

作者： Zhenguo Ma Bingrui Chen Yuan Xu Yangxiao Li Yatao Zhang School of Mechanical Electrical & Information Engineering Shandong University Weihai China School of Control Science and Engineering Shandong University Jinan China

In order to evaluate not only performance of iterative classifiers i.e. support vector machine (SVM), least-squares SVM (LS-SVM)) and non-iterative classifier (i.e. random forest (RM), but also efficiency of various feature schemes, the aforementioned machine learning algorithms were applied on six feature schemes of ECG recordings. The ECG recordings were obtained from the MIT-BIH normal sinus rhythm database, the MIT-BIH atrial fibrillation database and the MIT-BIH ST change database. These recordings were initially filtered by a 0.1 - 12 Hz band pass filter. Then 80 features including 48 time domain, 18 frequency domain, 12 time-frequency and 2 principle component analysis (PCA) features were extracted to construct six feature schemes. The SVM, LS-SVM and RF algorithms were used to discern normal, atrial fibrillation (AF) and ST change ECG recordings on the six feature schemes respectively. Finally experiment results illustrated that RF could yield the best highest F1 scores 0.8908 for the binary classification (i.e. normal and AF ECG groups) and 0.7535 for the multi-class classification (i.e. the normal, AF and ST change ECG groups).

关键词： Feature extraction Electrocardiography Support vector machines Time-domain analysis Radio frequency Time-frequency analysis

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