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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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Enhancing the Representation of Multiple Anatomical Network for Young Adults with Self-Esteem Difference

Enhancing the Representation of Multiple Anatomical Network ...

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

作者： Bo Peng Aditya Saxena Suhong Wang Yakang Dai Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China Trauma center Khandwa district hospital Khandwa India The Third Affiliated Hospital of Soochow University Changzhou China Jinan Guoke Medical Engineering Technology Development co. LTD Jinan China

Anatomical brain networks help explain how distributed brain area are connected, and the connectivity differences have been used to recognize neuroimaging biomarkers for mental and psychologic symptoms. However, most methods analyzing anatomical brain networks employ single-level network framework, which cannot give an overall representation on the subtle deviations of the whole brain. In this study, we put forward an enhancing representation of multiple anatomical networks for young adults with self-esteem differences. Specifically, we construct a four-layer network framework using both morphological features (i.e. gray matter volume, cortical thickness) for each region of interest (ROI) and hierarchical networks features. Two types of filter-based approaches are adopted, followed by a wrapper-based approach to select a set of distinctive features. Then, multi-kernel machine learning method is used to combine two kinds of features (ROI-based morphological features and hierarchical correlative features) into a single classifier. Our method achieves an accuracy of 97.26%, which has an outstanding performance compared with the single-network methods. This study gives a potential evidence that self-esteem is related to specific ROIs and anatomical brain connections.

关键词： Feature extraction Brain Support vector machines Surface treatment Kernel biomedical imaging Sensitivity

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Cardiovascular Tree Recognition and its Three-Dimensional Reconstruction from Monoplane X-Ray Angiography Based on Spatio-Temporal Constraints

Cardiovascular Tree Recognition and its Three-Dimensional Re...

引用

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

作者： Tianxu Zhang Qiang Liu Xinyu Zhang Zhenghua Huang Jiaojiao Zong Nong Sang School of Electrical and information Engineering Wuhan Institute of Technology Wuhan China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

In this paper, we propose a new method of collaborative matching based on spatio-temporal constraints (COMSTC) that can be used for three-mensional (3D) reconstruction of monoplane X-ray coronary angiography. The main idea of this method is that, under the guidance of the multi-scale sptio-temporal expectation model (MSSTEM), the constraint relationships such as the time of the contrast agent flow, the spatial distribution of vasculum primitives, anatomical structure, and topological similarity are collaboratively used to distinguish the homonymous vasculum primitive pairs of dual-perspective angiogram pairs of the sequence. As can be seen from the experimental results, the MSSTEM of the vascular tree in this paper is used to guide matching of the blood vessels systematically starting from the root, which solves the problem of individual diversity, and the controllability and robustness are satisfactory. Iterative matching and searching using the maximum potential descent method improve the matching accuracies of pairs of vasculum primitives and pairs of points on vasculum primitives, and guarantee a more reliable automatic 3D reconstruction of the dual-perspective angiography images.

关键词： Three-dimensional displays Angiography image reconstruction Bifurcation Solid modeling Two dimensional displays Optimized production technology

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A Co-Random Walks Segmentation Method for Aerial Insulator Video images

A Co-Random Walks Segmentation Method for Aerial Insulator V...

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

作者： Zihui Yin Rong Meng Junhu Dong Jingyi Lang Zhenbing Zhao State Grid Hebei Electric Power Co. Ltd. Maintenance Branch Shijiazhuang China State Grid Hebei Electric Power Co. Ltd Shijiazhuang China School of Electrical and Electronic Engineering North China Electric Power University Baoding China

Insulator segmentation is an important premise of automatic state detection and fault diagnosis in image processing. The aerial insulator images are characterized by complex background, low resolution, large number and many pseudo targets. The classical random walks algorithm may segment wrongly. It requires massive interaction to segment multiple images, which makes user fatigue and results in bad segmentation quality. This paper proposes an automatic co-segmentation method called co-random walks, which utilizes the relationship between aerial insulator video images as prior information to find corresponding seed points in order to achieve higher segmentation accuracy. Firstly, we remove the texts in original images, the preprocessed images are over-segmented into super-pixels by SLIC (Simple Linear Iterative Clustering) for fast segmentation. Then the collaborative graph network is constructed, we use a greedy algorithm to get corresponding seed points. Finally, the random walks segmentation for corresponding seed points of each image is performed. The experimental results show that the proposed method can efficiently distinguish the insulator from complex background and eliminate the pseudo target like tower. We only need a few seed points to achieve relatively high accuracy of automatic segmentation, which is instrumental to unmanned aerial vehiclel aerial insulators' state detection and fault diagnosis.

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Automatic Full and Partial Shoeprint Retrieval System for Use in Forensic Investigations

Automatic Full and Partial Shoeprint Retrieval System for Us...

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

作者： Hsin-Chuan Chiu Chung-Hao Chen Wen-Chao Yang Jiajun Jiang Forensic Science Division Yunlin County Police Bureau Yunlin County Taiwan R.O.C. Old Dominion University Norfolk VA U.S.A. National Central Police University Taoyuan City Taiwan R.O.C.

Forensic identification methods can be divided into physical forensics, chemical forensics, and biological forensics categories. Robust automatic identification or retrieval systems, such as AFIS and DNA-STR analysis systems, for fingerprints and biological evidence have been introduced. However, there are still challenges for shoeprint evidence. For example, most of identification or retrieval methods can only be applied to intact shoeprints, and do not compare shoeprints with different sizes. In addition, the shoeprints at a real crime scene are commonly incomplete, noisy, and unclear. In this paper, we propose a new shoeprint retrieval system that normalizes the size of shoeprints and is efficient in retrieving full- and partial-print shoeprints. Experimental results demonstrate our proposed method is capable of handling shoeprint identification in varied situations such as dusting environment, partial print, etc. In particular, our proposed method outperforms Ho's method when dealing with different shoe sizes.

关键词： Feature extraction Footwear Databases Forensics image resolution image edge detection Transforms

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Fissured Tongue image Recognition Based on Support Vector Machine

Fissured Tongue Image Recognition Based on Support Vector Ma...

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

作者： Chao Wan Yue Zhang Chunming Xia Peng Qian Yiqin Wang East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Health Identification and Assessment Shanghai University of Traditional Chinese Medicine Shanghai China

Tongue diagnosis is a primary method of traditional Chinese medicine (TCM) diagnosis, and the identification of fissured tongue is one of the important contents of tongue diagnosis, since fissured tongue always reflects some diseases. In this paper, the recognition of fissured tongue is studied. Firstly, the images of fissured tongue and non-fissured tongue were preprocessed by median filtering, histogram averaging and tongue segmentation. Because there are obvious texture and gray gradient differences between fissured and non-fissured areas in tongue images, local binary pattern (LBP), histogram of oriented gradient (HOG) and haar-like feature extraction were applied to tongue images respectively to get the input vectors. Then support vector machine (SVM) with four different kernel functions were respectively applied to train the classifiers and five-fold cross validation was adopted to get the average accuracy, precision and recall of the classification model. The results show that LBP features with linear kernel function can get the best classification effect, among which the accuracy rate is 97.72%, the precision rate is 97.46%, and the recall rate is 98.06%. This research lays a foundation for further fissure extraction in fissured tongue, and further facilitates the development of intelligence and automation of tongue diagnosis in the field of traditional Chinese medicine.

关键词： Tongue Feature extraction Kernel Support vector machines image segmentation Histograms Mathematical model

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Target Detection and Recognition Based on Convolutional Neural Network for SAR image 11

Target Detection and Recognition Based on Convolutional Neur...

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

作者： Wang, YanPing Zhang, YiBo Qu, HongQuan Tian, Qing North China Univ Technol Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781538676042

In the era of remote sensing big data, synthetic aperture radar (SAR) image interpretation is a great scientific application challenge [1]. The automatic detection and identification of targets are the hot spots and focuses of this research field. In general, the SAR image detection and recognition method require multiple independent steps such as filtering, edge extraction, image segmentation and target recognition. The complexity of the process not only reduces the efficiency of target detection and recognition of SAR images, but also loses a lot of key image features to a certain extent, which makes it difficult to further process SAR images and restrain the accuracy of detection and recognition. Therefore, this paper presents a method for automatic detection and recognition of SAR image targets based on convolution neural network (CNN). By using the CNN model to conduct target detection and recognition experiment on the moving and stationary target acquisition and recognition (MSTAR) benchmark dataset, we verify the validity of the deep neural network in the field of SAR image target recognition, which lays the foundation for further research in this field.

关键词： component synthetic aperture radar (SAR) convolution neural network (CNN) target detection and recognition accuracy

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A Comparison of Adaptive Boosting Algorithms for the Respiratory signal Prediction

A Comparison of Adaptive Boosting Algorithms for the Respira...

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

作者： Wenzheng Sun Gongqi Chen Mingyan Jiang Tao You The Second Affiliated Hospital of Medical College of Zhejiang University Hangzhou China School of Information Science and Engineering Shandong University Qingdao China The Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai China Affiliated Hospital of Jiangsu University Zhenjiang China

Dynamic tracking of tumor with radiation beam in radiation therapy requires prediction of real-time target location ahead of beam delivery as the treatment beam with beam or gating tracking brings in time latency. We proposed an adaptive boosting (Adaboost) method based on the multi-layer neural network (MLP-NN) to predict the respiratory signal accuracy in our previous study. Recently, several variants of Adaboost methods showed great potential for the regression prediction problem. Hence, we investigated the prediction performance of four popular adaptive boosting method based on the MLP-NN for the respiratory prediction problem in this study. The root-mean-square-error (RMSE), correlation coefficient (CC) and maximum error (ME) between predicted and real respiratory signals, obtained from the Real-time Position Management, were used to evaluated the prediction performance. The Adaboost. RT method and the Adaboost method used in our previous study get the best RMSE and CC while the Adaboost. BCC obtained the best ME. The experiment results demonstrated that appropriate Adaboost method based on MLP-NN could predict the respiratory signal accuracy.

关键词： Prediction algorithms Classification algorithms Training Training data Boosting Hospitals Real-time systems

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Identifying Compatible Syndrome Elements of Traditional Chinese Medicine Based on Three-Way Decisions

Identifying Compatible Syndrome Elements of Traditional Chin...

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

作者： JINGMIN LUAN YUHENG WANG LEI WANG XULONG LIU WENXUE HONG JIALIN SONG School of Computer and Communication Engineering Northeast University at Qinhuangdao Qinhuangdao Hebei P.R. China College of Humanities and Law YanShan University School of Electrical Engineering YanShan University Qinhuangdao Hebei P.R. China

Quantitative diagnosis of Traditional Chinese Medicine (TCM) is a hot issue in the study of modernization of TCM. Quantitative identification of syndrome elements is the key problem of quantitative diagnosis. The relationship between syndrome elements of TCM can be summarized as compatibility and incompatibility. Aiming at the identification of syndrome elements compatibility relationship, this paper presents an identification model based on Three-Way Decision. By constructing a double threshold evaluation function, the membership degree between samples and branches can be calculated, and the optimal decision function is finally provided. The method which this paper proposed has been tested in small sample clinical trials. 30 clinical cases have identified and analyzed by the method and a medical team of three traditional Chinese physicians, and the result shows that match degree has over 93 percent between the method and the medical team. Clinical preliminary experiment and result show that the proposed method is feasible and effective. The research provides a new approach for quantitative and artificial intelligence research of TCM.

关键词： Decision making Medical diagnostic imaging Mathematical model Finite element analysis Clinical trials Diseases

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Distributed Environmental Parameter Measurement System Based on TMS320F28335

Distributed Environmental Parameter Measurement System Based...

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

作者： Menglin Zhang Mengke Sun Pengtao Lai Zheng Zhu College of Physics and Optoelectronic Engineering Harbin Engineering University Harbin Heilongjiang China

With the development of society, people pay more and more attention to the influences of indoor environmental quality on physical health and work efficiency. At present, the indoor environment supervision instruments in the market usually have the disadvantages of small measurement range, imperfection of wireless collection and low precision of measurement. Therefore, a distributed environmental parameter measurement system based on TMS320F28335 is proposed in this thesis, which composes of environmental parameter measurement subsystem, control module based on DSP chip and LCD display system. The environmental parameter measurement subsystem collects environmental parameter data through temperature and humidity, illuminance, volatile organic compound (VOC), PM2.5, formaldehyde, and CO sensors;and transmits the collected data to control module via WiFi module;and the control module transmits the data to LCD display system via RS-232 interface to realize the real time display of the measurement results. This distributed environmental parameter measurement system can realize the real-time collection of different indoor environmental parameters in large range and wireless transmission, and provides real-time display on LCD screen.

关键词： Wireless fidelity Temperature sensors Temperature measurement Liquid crystal displays Control systems Sensor systems

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Detection of Workers Without the Helments in Videos Based on YOLO V3

Detection of Workers Without the Helments in Videos Based on...

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

作者： Jing Hu Xiaowei Gao Hefeng Wu Songhe Gao Power Research Institute of State Grid Jiangxi Electric Power Company Beijing Imperial Image Intelligent Technology Co. LTD

In the construction site with complex environment, there are a variety of potential risk factors threatening personal safety. Since the head is the most critical part of a human body and is the most vulnerable to fatal injuries, lots of accidents caused by workers not wearing safety helmets have occurred from time to time. So, the staff working in construction site has the requirement to wear the safety helmet. In order to reduce the incidence of safety accidents caused by not wearing a protective helmet, a realtime detection application for wearing helmet based on YOLO (You Look Only Once) v3 was proposed and encapsulated into a real-time detection software with alert function and simple efficient operation, which has been successfully deployed to several construction sites. YOLO v3 model has a good response to the identification of aśpesonas'. Therefore, firstly, the workers in video are identified and intercepted by YOLO v3 model, and positive and negative samples are made. Then the worker in the dataset is identified whether wearing helmets or not. Compared with y-OLO v3, the mAP (Mean Average Precision) reached 93.5%, and the detection rate reached 35fps. Theoretical analysis and experimental results show that the proposed algorithm not only satisfies the helmet wearing detection task in real time, but also has high detection accuracy.

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