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Proceedings of the 5th International Conference on Biomedical Signal and Image Processing最新文献

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Pre-cue EEG Rhythms Associated with MI-BCI Performance Variation 提示前脑电图节律与MI-BCI表现变化相关
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417556
Qing Zhou, Ying-Hao Jiang, Jiafan Lin, Lin Yao, Kedi Xu
{"title":"Pre-cue EEG Rhythms Associated with MI-BCI Performance Variation","authors":"Qing Zhou, Ying-Hao Jiang, Jiafan Lin, Lin Yao, Kedi Xu","doi":"10.1145/3417519.3417556","DOIUrl":"https://doi.org/10.1145/3417519.3417556","url":null,"abstract":"In recent years, Motor Imagery (MI) based Brain-Computer Interface (BCI) has been proven to be a promising approach for stroke neurorehabilitation. However, the potential therapeutic benefit was limited by BCI performance variation and the 'BCI-illiterate' problem, in which a non-negligible portion of users cannot reach adequate BCI control. In this paper, we investigated the MI-BCI performance variation across subjects (inter-subject) and within-subjects over an extended period (intra-subject). Eight subjects were recruited for a 7-session MI-based BCI intervention. The results showed that pre-cue theta and beta power were significantly correlated with BCI performance both inter and intra-subject aspects. The pre-cue rhythmic dynamic as a neurophysiological index would provide a way for future predicting and neurofeedback training to improve BCI performance.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131814469","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
BG-CNN: A Boundary Guided Convolutional Neural Network for Corneal Layer Segmentation from Optical Coherence Tomography 光学相干层析成像角膜层分割的边界引导卷积神经网络
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417560
Lei Wang, M. Shen, Qian Chang, Ce Shi, Yuheng Zhou, J. Pu
{"title":"BG-CNN: A Boundary Guided Convolutional Neural Network for Corneal Layer Segmentation from Optical Coherence Tomography","authors":"Lei Wang, M. Shen, Qian Chang, Ce Shi, Yuheng Zhou, J. Pu","doi":"10.1145/3417519.3417560","DOIUrl":"https://doi.org/10.1145/3417519.3417560","url":null,"abstract":"Precise segmentation of corneal layers depicted on optical coherence tomography (OCT) images plays an important role in detecting corneal diseases, such as keratoconus and dry eye. In this study, we present a boundary guided convolutional neural network (BG-CNN) to extract different corneal layers. The developed network uses three convolutional blocks to construct two network modules derived from the classical U-Net network. This network was trained based on a dataset consisting of 1712 OCT images. The segmentation results demonstrated the developed network achieved an average dice similarity coefficient (DSC) of 0.9599 and an interFiguresection over union (IOU) of 0.9253 on an independent testing set from our dataset, and it outperformed some available segmentation networks.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124408897","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
A Robust Optic Disc Localization Algorithm in Retinal Images Based on Support Vector Machine 基于支持向量机的视网膜图像鲁棒视盘定位算法
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417552
Zhiwei Zhou, Junyi Yao, Ling Wang
{"title":"A Robust Optic Disc Localization Algorithm in Retinal Images Based on Support Vector Machine","authors":"Zhiwei Zhou, Junyi Yao, Ling Wang","doi":"10.1145/3417519.3417552","DOIUrl":"https://doi.org/10.1145/3417519.3417552","url":null,"abstract":"Optic disk (OD) localization is a significant step when processing the retinal images in computer-aided diagnosis. In order to determine the location of OD precisely and robustly, an OD localization algorithm based on support vector machine (SVM) is proposed in this paper. According to some structural and intensity features of the bright regions in the retinal images, the SVM classifier is trained to recognize bright OD candidate regions. A convex hull is created on the basis of these candidate regions to locate the center of OD. Compared with OD localization methods in literatures, this proposed approach can locate the center of OD with higher accuracy because the application of machine learning algorithm improves the classification accuracy of bright regions. Three public databases with total 259 images were tested to evaluate the performance. The proposed method can achieve an accuracy of 100%, 96.9%, 97.8% for DRIVE database, DIARETDB0 database and DIARETDB1 database respectively.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129526075","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 Hybrid Network for H&N Organs at Risk Segmentation 一种新的H&N器官风险分割混合网络
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417522
Zesen Cheng, Tianyu Zeng, Sijuan Huang, Xin Yang
{"title":"A Novel Hybrid Network for H&N Organs at Risk Segmentation","authors":"Zesen Cheng, Tianyu Zeng, Sijuan Huang, Xin Yang","doi":"10.1145/3417519.3417522","DOIUrl":"https://doi.org/10.1145/3417519.3417522","url":null,"abstract":"In this paper, we find a network which can achieve better result than the state of the art for Head and Neck Organ at Risks (OARs) segmentation. At first, we enumerate the popular networks, and sum up their characteristic. We extract the main components from these popular networks and we design experiment to evaluate these components. We split the experiment into two stage. At the first stage experiment, we try to find out which components and constructions can let the network achieve better result than baseline model, Unet, for H&N OAR segmentation. After finding out the useful components and constructions, we try to mix them up to build a novel network which absorbs all their merits. At last, we get a hybrid network, Attention-W-net which gets the best result and defeat the state of the art. All networks are evaluated on 16th CSTRO conference H&N OAR segmentation competition dataset.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"506 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123064236","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
Multimodal Image Fusion Based on Random Projection and Joint Sparse Representation 基于随机投影和联合稀疏表示的多模态图像融合
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417520
Yanping Li, Yinmei He, L. Du
{"title":"Multimodal Image Fusion Based on Random Projection and Joint Sparse Representation","authors":"Yanping Li, Yinmei He, L. Du","doi":"10.1145/3417519.3417520","DOIUrl":"https://doi.org/10.1145/3417519.3417520","url":null,"abstract":"In this paper, a novel multimodal medical image fusion algorithm based on compressive sensing (CS) is provided, which combines random projection and joint sparse representation. First, the medical image data is encoded with random projection and joint sparse representation. Second, the images are fused by using sparse representation coefficients in the compression domain. The experimental results demonstrate that our algorithm can obtain the same image fusion quality with a much smaller compression ratio and fewer samples when compared to existing CS-based image fusion algorithms. In addition, our algorithm achieves flexible quality and computational complexity control by adjusting the compression ratio.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132726969","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 Real-time Depth of Anaesthesia Monitoring Method using Detrended Fluctuation Analysis and ANN 一种基于去趋势波动分析和神经网络的实时麻醉深度监测新方法
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3419403
Xing Chen, P. Wen
{"title":"A Novel Real-time Depth of Anaesthesia Monitoring Method using Detrended Fluctuation Analysis and ANN","authors":"Xing Chen, P. Wen","doi":"10.1145/3417519.3419403","DOIUrl":"https://doi.org/10.1145/3417519.3419403","url":null,"abstract":"In this paper, Detrended Fluctuation Analysis (DFA) method and artificial neural network (ANN) were applied to investigate the EEG variation during anesthesia. Rather than quantifying the loss of consciousness with a single feature, self-similarity, several cross-fluctuations from two channel raw EEG data using specific window sizes were extracted and combined to classify the patient anaesthesia state. The proposed method is evaluated using off-line data and the results are compared with the most widely used Bispectral (BIS) Index. In addition, the proposed method had reduced the calculation complexity for the real time implementation.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125652916","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
Real-time Fluorescent Image Analysis of SlipChip-based Microfluidic Devices 基于滑动芯片的微流控装置的实时荧光图像分析
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417521
D. García-Alonso, Haijun Qu, Lei Xu, F. Shen
{"title":"Real-time Fluorescent Image Analysis of SlipChip-based Microfluidic Devices","authors":"D. García-Alonso, Haijun Qu, Lei Xu, F. Shen","doi":"10.1145/3417519.3417521","DOIUrl":"https://doi.org/10.1145/3417519.3417521","url":null,"abstract":"This paper presents a real-time fluorescence analysis method for the characterization of nucleic acid amplification on SlipChip. SlipChip is a microfluidic device that can generate and manipulate aqueous droplets by the relative slipping movement of top and bottom microfluidic plates. This real-time fluorescence analysis method can perform background correction, retrieve the fluorescence signal from droplets, and provide kinetic analysis of fluorescence intensity over time on the SlipChip device. As a proof of concept, we demonstrated the efficacy of this fluorescence analysis method for analyzing polymerase chain reaction (PCR) kinetics with intercalation dye on the SlipChip device. An integrated fluorescence imaging instrument with a small footprint was developed and will be presented elsewhere. The integration of the SlipChip device, hardware instrument, and fluorescent analysis algorithm provides a powerful tool for nucleic acid analysis and in situ quality control by monitoring amplification kinetics. The fluorescence analysis methodology proposed in this paper can also potentially be used for image analysis on other microfluidic systems.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130232025","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
Discriminative Color Space Learning for Face Anti-Spoofing via Convolutional Neural Networks 基于卷积神经网络的人脸防欺骗判别色彩空间学习
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417561
Yifeng Ren, Dong Huang, Lei Sun, Zhe Liu, Qingyan Li
{"title":"Discriminative Color Space Learning for Face Anti-Spoofing via Convolutional Neural Networks","authors":"Yifeng Ren, Dong Huang, Lei Sun, Zhe Liu, Qingyan Li","doi":"10.1145/3417519.3417561","DOIUrl":"https://doi.org/10.1145/3417519.3417561","url":null,"abstract":"Face spoofing detection is gaining an increasing attention in the biometric research. Various approaches have been proposed in the literatures. In these methods, the color variation of facial regions, caused by the defect of medium of fake face, is a vitally important clue. The traditional color spaces (e.g. RGB, HSV and YCbCr) are used in many spoofing detection approaches, however, it is not very discriminative to distinguish real and fake faces in these existing color spaces. So, in this paper, we propose a novel method to learn a new color space, which is suitable for face anti-spoofing and can be discriminative between real and fake faces. Different from other color learning methods, our novel method is based on convolutional neural networks and can nonlinearly project the real and fake face images into a distinguishable color s-pace. Extensive experiments are conducted on two publicly available databases, showing very interesting performance compared to other existing color spaces and state-of-the-art.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127285761","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
Hybrid Method for Biomedical Image Poisson Denoising 生物医学图像泊松去噪的混合方法
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417553
V. E. Karnaukhov, A. Krylov, Yong Ding, Mylène C. Q. Farias
{"title":"Hybrid Method for Biomedical Image Poisson Denoising","authors":"V. E. Karnaukhov, A. Krylov, Yong Ding, Mylène C. Q. Farias","doi":"10.1145/3417519.3417553","DOIUrl":"https://doi.org/10.1145/3417519.3417553","url":null,"abstract":"Convolutional neural networks (CNN) have recently become the main tool for the problem of biomedical image denoising. Nevertheless, CNN-based methods strongly depend on the used training set, and even small differences in the input data can cause output disturbance. One of the possible ways to tackle this problem is to use hybrid denoising methods that include combinations of CNN and \"classical\" image denoising algorithms. In this paper we present a hybrid iterative DeepRed algorithm for Poisson and mixed Poisson-Gaussian denoising with an automatic choice of the number of iterations. The choice is based on the control of biomedical image structures. Here we analyze the presence of regular structures in the ridge areas at the difference between noisy and filtered images by a multiscale ridge based approach. It was also discovered that the application of the Anscombe transform in the hybrid method improves denoising results. Test results for retinal image dataset DRIVE and Set12 natural images show practical applicability of the method.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134481899","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
Analysis and Application of Optical Illusion Images 视错觉图像的分析与应用
Proceedings of the 5th International Conference on Biomedical Signal and Image Processing Pub Date : 2020-08-21 DOI: 10.1145/3417519.3417554
Dan Wang, P. Cao
{"title":"Analysis and Application of Optical Illusion Images","authors":"Dan Wang, P. Cao","doi":"10.1145/3417519.3417554","DOIUrl":"https://doi.org/10.1145/3417519.3417554","url":null,"abstract":"An optical illusion image has a variety of \"illusion image\" characteristics under the human visual feeling, which is frequently used in some special application scenarios. This paper is through the study of its imaging principle, and then studied its innovative application. First of all, based on the research of visual illusions in various fields, such as neurology, psychology, vision and design, a rotating illusion image is constructed, and the influencing factors of rotation illusion are analyzed. Then, the optical flow constraint equation, the visual perception spatial frequency sensitivity function and the mathematical relationship between the image spatial frequency and the visual spatial frequency are studied. The visual perception gradient sensitivity function is calculated, and the influence of various characteristic parameters of the basic image elements on the visual illusion is tested. The experimental results show that the motion of visual illusion image is related to six factors, such as, the visual distance, vector gradient and saccade speed.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114163929","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
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