Proceedings of the 6th International Conference on Biomedical Signal and Image Processing最新文献_第2页

Compact Breathing Sensor with Humidified Air Delivery 紧凑型呼吸传感器加湿空气输送

Proceedings of the 6th International Conference on Biomedical Signal and Image Processing Pub Date : 2021-08-20 DOI: 10.1145/3484424.3484430

Lulu Wang, A. Al-Jumaily, Oleksiy Kuleshov, S. Ponomarenko, E. Khutoryan

{"title":"Compact Breathing Sensor with Humidified Air Delivery","authors":"Lulu Wang, A. Al-Jumaily, Oleksiy Kuleshov, S. Ponomarenko, E. Khutoryan","doi":"10.1145/3484424.3484430","DOIUrl":"https://doi.org/10.1145/3484424.3484430","url":null,"abstract":"Piezoelectric actuators as fans for air delivery are very attractive due to their ultra-lightweight, small power consumption, and low noise. Therefore, recent studies were taken on piezoelectric actuators as a potential alternative to air compressor/blower for biomedical devices. In this application, such as breathing devices, the airflow to the patient's mouth needs to be humidified. This study attempts to develop an integrated piezoelectric actuator-based breathing device to generate humidified airflow. The integrated system comprises a piezo fan for generating actuation and airflow. The moisture from the surface of the actuator blade is also transported to combine with airflow for humidified air delivery. The micro-structured actuator blade is acting as a moisture reservoir, and water is continuously supplied through the micro-tube to the surface of the blade. The continuous actuation of the blade establishes constant water evaporation from the surface of the actuator blade. The evaporated water vapor from the blade surface can easily be combined with delivered airflow by the actuator blade. The optimum humidified airflow can be obtained by an appropriate design (size and shape of the fan, moisture carrier/reservoir) of integrated piezoelectric actuator devices. This work demonstrates the fabrication process and the device characterization by measuring the delivered airflow and optimum resonance frequencies. This novel technique is very object-oriented for miniaturized breathing devices and very promising for biomedical applications.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132410066","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 Design of Zynq-based Medical Image Edge Detection Accelerator 基于zynq的医学图像边缘检测加速器设计

Proceedings of the 6th International Conference on Biomedical Signal and Image Processing Pub Date : 2021-08-20 DOI: 10.1145/3484424.3484434

Bin Li, Jingxian Chen, Xuejun Zhang, Xianfu Xu, Yini Wei, Deyu Kong

{"title":"A Design of Zynq-based Medical Image Edge Detection Accelerator","authors":"Bin Li, Jingxian Chen, Xuejun Zhang, Xianfu Xu, Yini Wei, Deyu Kong","doi":"10.1145/3484424.3484434","DOIUrl":"https://doi.org/10.1145/3484424.3484434","url":null,"abstract":"Edge detection technology plays an important role in medical image processing. Sobel operator edge detection is one of the commonly used edge detection operators. At present, most of the solutions using Sobel operator for edge detection of medical images are based on CPU and GPU. Processing speed can become a serious problem as image data increases. The acceleration effect of FPGA on edge detection is quite significant. However, the traditional Sobel edge detection scheme based on FPGA is developed by hardware description language, which has high requirements for developers and is very unfavorable to debugging. Using the Zynq series of C/C++ programming for acceleration can perfectly solve the above problems. However, the current Zynq-based Sobel operator edge detection research, only horizontal edge and vertical edge detection. In order to extract more edge details from different angles, we proposed an improved Sobel operator based on Zynq to detect edges. The performance of the proposed improved Sobel algorithm and the conventional Sobel algorithm on CPU and Zynq platform is compared and evaluated in detail. Experimental results show that the proposed scheme can extract more edge details and achieve satisfactory acceleration effect.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125286804","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

Glioma Image Segmentation Method on Fully Convolutional Neural Network 基于全卷积神经网络的神经胶质瘤图像分割方法

Proceedings of the 6th International Conference on Biomedical Signal and Image Processing Pub Date : 2021-08-20 DOI: 10.1145/3484424.3484432

Lin Chen, Qihong Liu, Kai Liu, Jie Lu, Limin Song, Kenan Yang

{"title":"Glioma Image Segmentation Method on Fully Convolutional Neural Network","authors":"Lin Chen, Qihong Liu, Kai Liu, Jie Lu, Limin Song, Kenan Yang","doi":"10.1145/3484424.3484432","DOIUrl":"https://doi.org/10.1145/3484424.3484432","url":null,"abstract":"Aiming at the difference in the segmentation performance of the three segmentation target regions in the glioma image segmentation task based on the fully convolutional neural network, we propose a comprehensive evaluation method of neural network performance based on four evaluation indices. In addition, we analyze the performance and characteristics of neural network in the segmentation task of glioma, study the segmentation performance of neural network in the whole tumor (WT), tumor core (TC) and enhanced tumor (ET) regions, and propose a deep learning algorithm based on multiple networks in parallel. In this paper, the input image of the two-dimensional neural network is sliced, and the input of the three-dimensional neural network is processed in two ways: overlapping and non-overlapping, and in the image post-processing part, the three-dimensional image is reconstructed before the evaluation index is calculated. This article uses four evaluation indexes, which are Dice, Sensitivity, PPV, and Hausdorff, for the three segmentation target regions, and performs RSR* weight calculation, and finally performs a comprehensive evaluation. Experimental results show that Vnet has the best comprehensive segmentation performance, FCN-8s has the best segmentation performance in the TC area, Unet++ has the best segmentation performance in the ET area, and Vnet has the best segmentation performance in the WT area. Based on this, we propose a FUV multi-network parallel algorithm, combined with a reverse attention mechanism to improve the segmentation accuracy of the three segmentation target regions.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124653990","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

A Multi-Scale and Multi-Resolution Approach for Liver Tumor Segmentation in CT Scans 肝脏肿瘤CT多尺度多分辨率分割方法研究

Proceedings of the 6th International Conference on Biomedical Signal and Image Processing Pub Date : 2021-08-20 DOI: 10.1145/3484424.3484431

A. Orazbayev, Huer Wen

引用次数: 1