2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第9页

Image Contrast Enhancement Using Weighted Uniform Histogram for Maximum Information 利用加权均匀直方图增强图像对比度以获取最大信息

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.2018.8633178

N. Kwok, Haiyan Shi, Ying-Hao Yu, Yeping Peng, Shilong Liu, Ruowei Li, Hongkun Wu

引用次数: 0

Top-Down Saliency Object Localization Based on Deep-Learned Features 基于深度学习特征的自顶向下显著性对象定位

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.2018.8633218

Duzhen Zhang, Shu Liu

引用次数: 3

PoseLab: A Levenberg-Marquardt Based Prototyping Environment for Camera Pose Estimation PoseLab:一个基于Levenberg-Marquardt的相机姿态估计原型环境

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.2018.8633112

M. Darcis, W. Swinkels, A. E. Guzel, L. Claesen

引用次数: 5

Complexity Analysis of EEG Signals for Fatigue Driving Based on Sample Entropy 基于样本熵的疲劳驾驶脑电信号复杂度分析

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.2018.8633011

Chunxiao Han, Yaru Yang, Xiaozhou Sun, Yingmei Qin

{"title":"Complexity Analysis of EEG Signals for Fatigue Driving Based on Sample Entropy","authors":"Chunxiao Han, Yaru Yang, Xiaozhou Sun, Yingmei Qin","doi":"10.1109/CISP-BMEI.2018.8633011","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633011","url":null,"abstract":"Sample entropy describes the complexity of time series by information growth rate, which has been widely used in EEG signal analysis. In order to explore the brain activity during fatigue driving, we built a simulated automobile driving experimental platform based on Unity3D software, and designed an experiment that simulating fatigue driving process to collects EEG signals of the brain from 17 healthy subjects. The changes of the complexity of the EEG signals in different rhythms are studied by comparing the sample entropy of different regions during the sober and fatigue states, respectively. The results show that the sample entropy of the EEG signals of the brain in the delta, theta, alpha, beta and gamma rhythms decrease during fatigue in which the beta rhythm and gamma rhythm decrease significantly. The sample entropy of frontal region of the brain in beta rhythm decrease significantly during fatigue state, and alpha, beta and gamma rhythm of central region of brain also decrease significantly during fatigue state, while there is no significant change in other brain regions. This experiment shows that the randomness of nerve cell activity is small and the complexity of brain decreases during fatigue state, which mainly manifest in that the beta rhythm of frontal and central regions is significantly decreased, which can provide a theoretical support for fatigue driving detection.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"85 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":"126297323","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: 5

3D Head Shape Analysis of Suspected Zika Infected Infants 疑似寨卡病毒感染婴儿的三维头部形状分析

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.2018.8633125

X. Ju, I. G. Junior, Leonardo de Freitas Silva, P. Mossey, Dhelal Al-Rudainy, A. Ayoub, Adriana Marques De Mattos

{"title":"3D Head Shape Analysis of Suspected Zika Infected Infants","authors":"X. Ju, I. G. Junior, Leonardo de Freitas Silva, P. Mossey, Dhelal Al-Rudainy, A. Ayoub, Adriana Marques De Mattos","doi":"10.1109/CISP-BMEI.2018.8633125","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633125","url":null,"abstract":"The babies infected from Zika before they are born are at risk for problems with brain development and microcephaly. 3D head images of 43 Zika cases and 43 controls were collected aiming to extract shape characteristics for diagnosis purposes. Principal component analysis (PCA) has been applied on the vaults and faces of the collected 3D images and the scores on the second principal components of the vaults and faces showed significant differences between the control and Zika groups. The shape variations from $-2sigma text{to} 2sigma$ illustrated the typical characteristics of microcephaly of the Zika babies. Canonical correlation analysis (CCA) showed a significant correlation in the first CCA variates of face and vault which indicated the potential of 3D facial imaging for Zika surveillance. Further head circumferences and distances from ear to ear were measured from the 3D images and preliminary results showed the adding ear to ear distances for classifying control and Zika children strengthened the abilities of tested classification models.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"43 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":"125590375","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

Challenges and Preprocessing Recommendations for MADCAT Dataset of Handwritten Arabic Documents MADCAT阿拉伯语手写文档数据集的挑战与预处理建议

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.2018.8633103

Gheith A. Abandah, Ahmad S. Ai-Hourani

引用次数: 1

Segmentation of Mammography Images Based on Spectrum Clustering Method 基于谱聚类方法的乳腺造影图像分割

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.2018.8633062

Silin Liu, Y. Wang

{"title":"Segmentation of Mammography Images Based on Spectrum Clustering Method","authors":"Silin Liu, Y. Wang","doi":"10.1109/CISP-BMEI.2018.8633062","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633062","url":null,"abstract":"Mass segmentation in mammography images is one of the effective ways to screen breast cancer. The accurate segmentation of the pectoral muscle can improve the accuracy of mass recognition. However, the results of traditional mammography image segmentation methods often appear incomplete segmentation and over-segmentation, the accuracy is low, which directly affects the accuracy of breast cancer screening. To solve these problems, a segmentation method of mammography images based on spectral clustering is proposed in this paper. Firstly, we use the spectral clustering to segment the pectoral muscle preliminarily. In view of the stratification of pectoral muscle and the unclear boundary of breast muscle and breast tissue, we use the maximum grayscale difference constraint and shape constraint to achieve accurate breast muscle segmentation. The mass is recognized accurately with the segmented image. The experimental results of the MIAS breast image database show that the proposed method can effectively segment the uneven grayscale pectoral muscle caused by the overlap of the pectoral muscle tissues, and it is robust to the segmentation of tumors of different sizes.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"76 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":"115013063","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

Detection and Analysis of Breathing Sounds from Trachea 气管呼吸音的检测与分析

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.2018.8633198

Xiong Li, Seung H. Lee, Soo M. Chae, Kyoung H. Hwang, Moo J. Cho, J. Im

引用次数: 0

Research of EEG Signal Based on Permutation Entropy and Limited Penetrable Visibility Graph 基于置换熵和有限穿透可见图的脑电信号研究

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.2018.8633121

Honghong Xu, Jiafei Dai, Jin Li, Jun Wang, F. Hou

{"title":"Research of EEG Signal Based on Permutation Entropy and Limited Penetrable Visibility Graph","authors":"Honghong Xu, Jiafei Dai, Jin Li, Jun Wang, F. Hou","doi":"10.1109/CISP-BMEI.2018.8633121","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633121","url":null,"abstract":"Complex networks can be seen as a way of describing complex systems. Starting from the end of the twentieth Century, the theory of complex network has gradually penetrated into all fields of social science, and it has become one of the most important tools for people to solve the problem. The complex network theory is helpful in studying the interaction between different brain regions, topology structure and the dynamic information, as well as the relationship between disease and physiological function. Electroencephalogram(EEG) is an important tool for the disease diagnosis and prediction. The paper adopts Permutation Entropy(PE) and Limited Penetrable Visibility Graph(LPVG) algorithm to construct the complex networks and implement networks visualization. Using this method to research 21 normal people and 21 epilepsy EEG signal, in addition compare statistical characteristics of different brain networks. The results verify the validity of the PE and LPVG algorithm for analyzing brain functional networks and show that the properties of the different attention EEG are different. This method provides important reference for further study of the brain function network dynamics of epileptic EEG signals.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"25 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":"132974276","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: 2

A 3D Fully Convolutional Network Based Semantic Segmentation for Ear Computed Tomography Images 基于三维全卷积网络的耳部计算机断层图像语义分割

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.2018.8633242

Zhaopeng Gong, Xiaoguang Li, Li Zhou, Hui Zhang

{"title":"A 3D Fully Convolutional Network Based Semantic Segmentation for Ear Computed Tomography Images","authors":"Zhaopeng Gong, Xiaoguang Li, Li Zhou, Hui Zhang","doi":"10.1109/CISP-BMEI.2018.8633242","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633242","url":null,"abstract":"Ear computed tomography (CT) has become an important means of diagnosing ear diseases, which provides doctors with a chance of observing the shape and components of the key anatomical structures of the auditory system. Therefore, it is helpful to diagnose the ear diseases early. However, the anatomical structures of the auditory system are characterized by complexity, sophisticated, and large individual differences, meanwhile, they are small and difficult to segment. Most of the existing medical image segmentation algorithms fail in segmenting the ear anatomical structures. To address the problem, a 3D fully convolutional network (3D- FCN) based semantic segmentation method is proposed for the key anatomical structures of ear CT Images. We evaluated our approach on the ear CT dataset. Compared to the 2D fully convolutional network (2D-FCN), the mean Dice-Serensen Coefficient (DSC) of our method is improved significantly in the task of segmentation for six key anatomical structures of the ear. The experimental results show that our method can effectively improve the segmentation accuracy of key anatomical structures of ear CT images.","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":"131130455","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