2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第8页

Repair of Algebraic Geometry Codes of Two Failed Nodes 两个失效节点代数几何码的修复

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633209

Liangwu Cui, Wenwen Chen, Shuai Hu

引用次数: 0

Automation of the ACR MRI Low-Contrast Resolution Test Using Machine Learning 利用机器学习实现ACR MRI低对比度分辨率测试的自动化

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633140

J. E. Ramos, H. Y. Kim, F. Tancredi

{"title":"Automation of the ACR MRI Low-Contrast Resolution Test Using Machine Learning","authors":"J. E. Ramos, H. Y. Kim, F. Tancredi","doi":"10.1109/CISP-BMEI.2018.8633140","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633140","url":null,"abstract":"Magnetic Resonance Imaging (MRI) is a powerful, widespread and indispensable medical imaging modality. The American College of Radiology (ACR) recommends weekly acquisition of phantom images to assess the quality of scanner. Usually, these images must be analyzed by experienced technicians. Automatic analysis of these images would reduce costs and improve repeatability. Some automated methods have been proposed, but the automation of two of the ACR image quality tests remains open problem. Reports on the high- and low-contrast resolution tests are scarce and so far none of the proposed methods produce results robust enough to allow replacing human work. We use Machine Learning to emulate, with high accuracy, the detection of 120 low-contrast structures of ACR phantom by an experienced professional. We used a database with 620 sets of ACR phantom images that were acquired on scanners of different vendors, fields and coils, totaling 74,400 low-contrast structures. Technicians with more than 10 years of experience labeled each structure as ‘detectable’ or ‘undetectable’. Machine learning algorithms were fed with image features extracted from the structures and their surroundings. Among the five methods we tested, Logistic Regression yielded the largest area under the ROC curve (0.878) and the highest Krippendorff(s alpha (0.995). The results achieved in this study are substantially better than those previously reported in the literature. They are also better than the classifications made by junior technicians (with less than 5 years of experience). This indicate that the ACR MRI low-contrast resolution test may be automated using Machine Learning.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127983983","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: 3

Line Feature Extraction from RGB Laser Point Cloud RGB激光点云的线特征提取

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633181

Xujie Kang, Jing Li, Xiangtao Fan

引用次数: 4

Human Brain Activation Neuronal Substrates of Word Priming Effect: An fMRI Study 人脑激活词启动效应神经元基质的fMRI研究

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633073

Xiujun Li, Jingjing Yang, Qi Li, Dan Tong, Jinglong Wu

{"title":"Human Brain Activation Neuronal Substrates of Word Priming Effect: An fMRI Study","authors":"Xiujun Li, Jingjing Yang, Qi Li, Dan Tong, Jinglong Wu","doi":"10.1109/CISP-BMEI.2018.8633073","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633073","url":null,"abstract":"We investigated the differences in behavior and brain activity associated with Japanese word priming in Japanese word stem completion (WSC) tasks. In contrast, we use Korean characters as another task of recognizing tasks in graphic form. The behavioral results show that the performance of the main body has a significant role in promoting. Under the condition of not starting word (P/N), the accuracy rate is 94%, while the P/Y condition for word initiation is 100%. Reaction time under the P/N conditions is 1501ms, Reaction time under the P/N conditions is 978ms, and reaction time under the non-word P/Y conditions is 3106ms. The corresponding behavioral tests were performed in functional magnetic resonance imaging, and similar results were obtained. The results of fMRI showed that the activation of left and right middle frontal lobe and inferior frontal gyrus included right hemisphere lesions, upper and lower gyrus and auxiliary motor areas. We observed that the frontal lobe parietal network was consistent with the activation area in the English stem task.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128592492","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

Internet of Things in Centralized Management of Medical Equipment 物联网在医疗设备集中管理中的应用

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633227

Wenpo Yao, Min Wu, Jun Wang

引用次数: 0

Low-Light Image Enhancement Based on Joint Generative Adversarial Network and Image Quality Assessment 基于联合生成对抗网络和图像质量评估的弱光图像增强

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633150

Wei Hua, Youshen Xia

引用次数: 4

An Improved Otsu Multi-Threshold Image Segmentation Algorithm Based on Pigeon-Inspired Optimization 基于鸽子启发优化的改进Otsu多阈值图像分割算法

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633236

W. Liu, Heng Shi, Shang Pan, Y. Huang, Yingbin Wang

{"title":"An Improved Otsu Multi-Threshold Image Segmentation Algorithm Based on Pigeon-Inspired Optimization","authors":"W. Liu, Heng Shi, Shang Pan, Y. Huang, Yingbin Wang","doi":"10.1109/CISP-BMEI.2018.8633236","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633236","url":null,"abstract":"Threshold segmentation is a simple and effective method in the field of image segmentation which has the widest application domain. And the improvement of efficiency and precision of the threshold segmentation has received extensive attention and research. Inspired with the bio-inspired intelligent optimization, this paper proposes an Otsu multi-threshold segmentation based on pigeon-inspired optimization. The basic idea of this method is: the Otsu multi-threshold segmentation method is used to design the objective function, and the interclass variance function is used as the fitness function. The iterative optimization process is performed by the pigeon-inspired optimization. In this process, the fitness function is used as a criterion for the solution and corresponds to the coordinate of pigeon in the pigeon-inspired optimization. The best segmentation threshold group is obtained when the pigeon finds the global best position. This method converts the problem of finding the optimal solution into the solving problem of multidimensional variables and effectively optimizes the solution process. For the purpose of verifying the feasibility and segmentation accuracy of this method, the multiple segmentation parameters of several classical images of this method are compared with parameters of other classic algorithms such as particle swarm optimization and fireworks algorithm. The experiments show that the improved Otsu segmentation method based on pigeon-inspired optimization can effectively improve the speed of threshold solution, and the double operators ensures the accuracy of the segmentation. The method has the advantages of superior convergence and convenience of implementation. Simultaneously, the segmentation effect is ideal with this modus.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114682666","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: 9

Identification of Spam Based on Dependency Syntax and Convolutional Neural Network 基于依赖句法和卷积神经网络的垃圾邮件识别

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633016

Qing Yu, R. Liu

{"title":"Identification of Spam Based on Dependency Syntax and Convolutional Neural Network","authors":"Qing Yu, R. Liu","doi":"10.1109/CISP-BMEI.2018.8633016","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633016","url":null,"abstract":"Convolution Neural Network (CNN) is an algorithm which is more suitable for classify in images and natural language recognition. For Chinese spam processing identify, this paper proposed a hybrid DDTV-CNN model for short text classification that combines deep dependency trait vectorization (DDTV) with convolutional neural network. Parse the semantics of short texts by dependency parsing, we can get a binary tree, and construct a matrix through arc in a binary tree; then, nonlinear decomposing the matrix to get the eigenvector representation of semantic; finally, divide it into two categories by convolutional neural network. This article uses the performance evaluation index commonly used in the field of text classification and information retrieval to establish a evaluation system of spam identification. The evaluation system is used to evaluate the experimental data obtained from simulation experiments, and use performance evaluation index to evaluate that often used in text classification and domain of information retrieval, we construct evaluation system through it about spam identification; and then use it to evaluate experimental data that acquire from simulation experiment, and choice appropriate kernel functions and its parameters. Through the experiment contrasts, the classifier based on DDTV-CNN is more effective and rapid than traditional.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125370470","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 Novel Video Segmentation System Based on Viewpoints 一种基于视点的视频分割系统

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633135

Zijuan Cheng, Fang Meng

引用次数: 0

Voltage and Current Bi-Mode Stimulation Circuit of Low-Power Deep Brain Stimulator 低功率深部脑刺激器的电压电流双模刺激电路

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633184

Zhaonan Qin, Xiang Chen, Hua Jin, Jin Li

引用次数: 0