2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第7页

Improved human identification method based on electrocardiogram using ensemble empirical mode decomposition and Teager Energy Operator 基于集合经验模态分解和Teager能量算子的改进心电图人体识别方法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302224

Yanjun Deng, Zhidong Zhao, Yefei Zhang, Diandian Chen

{"title":"Improved human identification method based on electrocardiogram using ensemble empirical mode decomposition and Teager Energy Operator","authors":"Yanjun Deng, Zhidong Zhao, Yefei Zhang, Diandian Chen","doi":"10.1109/CISP-BMEI.2017.8302224","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302224","url":null,"abstract":"The purpose of this research is to develop a biometric system for individual identification with the electrocardiogram (ECG) signal. The ECG signal varies from person to person and it can be used as a new biometric for individual identification. This paper presents a robust preprocessing stage to eliminate the effect from noise and heart rate. A new feature extraction technique known as Ensemble Empirical Mode Decomposition (EEMD) with Teager Energy Operator (TEO) is derived and used to generate novel ECG feature vectors. The dimensionality reduction method Principal Component Analysis (PCA) is used reduce the feature space before classification. Finally, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithm are chosen as the classifiers. The proposed method is validated by experiments on 40 subjects from three public databases; the experiment results show that the subject recognition rate achieves 95.5% and 97.5% with KNN and SVM classifier respectively. For larger changes in heart rate, it also shows strong stability.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90856597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A classification model for the prostate cancer based on deep learning 基于深度学习的前列腺癌分类模型

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302240

Y. Liu, Xiaomei An

{"title":"A classification model for the prostate cancer based on deep learning","authors":"Y. Liu, Xiaomei An","doi":"10.1109/CISP-BMEI.2017.8302240","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302240","url":null,"abstract":"Regarded as one of the common cancers, the prostate cancer is a main reason harming the health of senile men, especially in Europe and the United States. In China, with increasing of living standards and aging of populations, the incidence of prostate cancer has an upward and younger tendency. Early detection and early treatment are helpful to patients. In the imaging diagnosis methods of prostate, magnetic resonance imaging (MRI) has been recognized as the most effective way. Whereas MRI image has several specialized configurations with a lot of medical information, and diagnosis results have a strong relationship with doctor's professional skill and experience, which makes the diagnosis for prostate cancer more difficult. Based on deep learning and the Convolutional Neural Networks (CNN), an image classification model which can provide some diagnosis classification reference was proposed in this paper. The data sets used in this paper consisted of 10056 diffusion weighted magnetic resonance imaging (DWI) images. Three quarters of the images were used for training and the rest images for testing. Experiments show that the accuracy rate of training set is 80.1539%, and the accuracy rate of testing set is 78.1538%. The curves of testing accuracy rate and loss show that this model has been trained steadily. The accuracy rate for single images are above 64.91%, and some may reach 99.99%. With certain clinical application value, this deep learning method can be widely applied to the grading and staging of prostate cancer and other cancer tasks.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"36 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91349742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Research and implementation of keyword extraction algorithm based on professional background knowledge 基于专业背景知识的关键词提取算法的研究与实现

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302332

Xuekun Zhang, Jing An, Wen Liu

引用次数: 2

Multi-element ultrasound transducer positioning method in MR-guided phased HIFU system 磁共振引导相控HIFU系统中多单元超声换能器定位方法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302081

Wanyan Wu, G. Shen, Bo Wei, A. Runa, Hongyang Mao, Yazhu Chen

{"title":"Multi-element ultrasound transducer positioning method in MR-guided phased HIFU system","authors":"Wanyan Wu, G. Shen, Bo Wei, A. Runa, Hongyang Mao, Yazhu Chen","doi":"10.1109/CISP-BMEI.2017.8302081","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302081","url":null,"abstract":"Phased High Intensity Focused Ultrasound (pHIFU) is an efficacious tool toward treating a variety of tumors, where procedure must be performed under Magnetic Resonance (MR) guidance. Because the MR system and the phased High Intensity Focused Ultrasound (pHIFU) system have separated coordinate systems independently, the registration of two coordinate system has to be done. To find the correct coordinate transformation matrix will affect the accuracy of the following treatment. A new method is proposed in this study to calculate the transformation matrix using specially designed markers and corresponding algorithms. Series of markers (>4) on the ultrasound probe are introduced which can be identified in the MR images. As only four markers which are not on the same plane are needed theoretically, algorithms are proposed to handle the problem of information redundancy and also further improve the accuracy of the calculation. The experiments using the proposed methods showed better results than those without using the redundant information.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"284 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76995681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Urban plane intersection fuzzy control and simulation 城市平面交点模糊控制与仿真

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302119

Yu Su

引用次数: 1

Design and implementation of a virtual-real interaction system 虚实交互系统的设计与实现

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302079

Fengquan Zhang, Yujie Zhao, Jinhong Li

引用次数: 0

A fast auto recognition algorithm for lunar terrain in wavelet domain 基于小波域的月球地形快速自动识别算法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301992

Jiarui Liang, Xiaolin Tian

引用次数: 1

The design and clinical application of sleep disorder treatment system 睡眠障碍治疗系统的设计与临床应用

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302256

Jun-an Zhang, G. Mo, Ke Zhang

引用次数: 1

The spectral matching algorithm based on hyperbolic mahalanobis metric 基于双曲马氏度量的光谱匹配算法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302019

Wenxia Bao, Guo-fen Yu, Gensheng Hu, Dong Liang, Shaokui Yan

{"title":"The spectral matching algorithm based on hyperbolic mahalanobis metric","authors":"Wenxia Bao, Guo-fen Yu, Gensheng Hu, Dong Liang, Shaokui Yan","doi":"10.1109/CISP-BMEI.2017.8302019","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302019","url":null,"abstract":"In order to solve the problem of the traditional image matching algorithm based on the spectral feature, the spectral matching algorithm based on hyperbolic mahalanobis metric is proposed in this paper. The algorithm first introduces a hyperbolic metric that has better adaptability to the sample data. And the hyperbolic mahalanobis metric is defined according to the statistical properties of the data. For a point in a given pointset, the sub point-set is selected according to the hyperbolic mahalanobis metric and the weighted graph of the sub point-set is constructed. The eigenvalue vector and the spectral gap vector are obtained by the singular value decomposition (SVD) of the adjacency matrix of the weighted graph, which construct the hyperbolic mahalanobis metric spectral feature. Finally, the matching matrix is constructed based on the similarity between the hyperbolic mahalanobis metric spectral feature and geometric relations between feature points. Thereby establish the matching mathematical model and introduce the greedy algorithm to obtain the matching results. A large number of experimental results show that the proposed algorithm improves the matching accuracy and the robustness. And the algorithm extends the application range of the spectral matching algorithm.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"5 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78473799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reversible watermarking algorithm for high efficiency video coding 一种高效视频编码的可逆水印算法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301931

Chunting Cai, Gui Feng, Chi Wang, Xue Han

引用次数: 6