2017 IEEE International Conference on Imaging Systems and Techniques (IST)最新文献_第6页

Temporal difference method based on positive and negative energy distribution in moving objects detection 基于正负能量分布的时间差分法在运动目标检测中的应用

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261473

Fu Feiran, Fang Ming, Yang Huamin

引用次数: 2

Liver sub-pixel detection method on magnetic resonance images 肝脏核磁共振图像亚像素检测方法

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261457

Suo Qinghua, Y. Xuedong, L. Juntao, Zhao Yan

引用次数: 0

A deep CNN based multi-class classification of Alzheimer's disease using MRI 基于深度CNN的MRI阿尔茨海默病多类分类

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261460

Ammarah Farooq, SyedMuhammad Anwar, Muhammad Awais, S. Rehman

{"title":"A deep CNN based multi-class classification of Alzheimer's disease using MRI","authors":"Ammarah Farooq, SyedMuhammad Anwar, Muhammad Awais, S. Rehman","doi":"10.1109/IST.2017.8261460","DOIUrl":"https://doi.org/10.1109/IST.2017.8261460","url":null,"abstract":"In the recent years, deep learning has gained huge fame in solving problems from various fields including medical image analysis. This work proposes a deep convolutional neural network based pipeline for the diagnosis of Alzheimer's disease and its stages using magnetic resonance imaging (MRI) scans. Alzheimer's disease causes permanent damage to the brain cells associated with memory and thinking skills. The diagnosis of Alzheimer's in elderly people is quite difficult and requires a highly discriminative feature representation for classification due to similar brain patterns and pixel intensities. Deep learning techniques are capable of learning such representations from data. In this paper, a 4-way classifier is implemented to classify Alzheimer's (AD), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and healthy persons. Experiments are performed using ADNI dataset on a high performance graphical processing unit based system and new state-of-the-art results are obtained for multiclass classification of the disease. The proposed technique results in a prediction accuracy of 98.8%, which is a noticeable increase in accuracy as compared to the previous studies and clearly reveals the effectiveness of the proposed method.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132132665","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: 186

High-speed railway catenary components detection using the cascaded convolutional neural networks 基于级联卷积神经网络的高速铁路接触网成分检测

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261459

Junwen Chen, Zhigang Liu, Hongrui Wang, Kai Liu

引用次数: 9

Ultrasonic spectral analysis for biomedical imaging 用于生物医学成像的超声光谱分析

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261549

Jianguo Ma, Min Wei, Lijun Xu, Boya Chen, Yulin Liu, Jie Du, Zijie Fang

引用次数: 1

Simulation on applying Gabor filtering method to direct laser absorption spectroscopy Gabor滤波法在直接激光吸收光谱中的应用仿真

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261532

H. Cui, Fei Wang

引用次数: 0

Phase aberration correction by bent-ray tracing method for ultrasound computed tomography 超声计算机断层扫描弯曲射线追踪法相位像差校正

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261510

Xiaolei Qu, S. Fan, T. Azuma

{"title":"Phase aberration correction by bent-ray tracing method for ultrasound computed tomography","authors":"Xiaolei Qu, S. Fan, T. Azuma","doi":"10.1109/IST.2017.8261510","DOIUrl":"https://doi.org/10.1109/IST.2017.8261510","url":null,"abstract":"Ultrasound computed tomography (USCT) is a promising technique for breast cancer detection. It can provide three modalities: sound speed image, attenuation image, and reflection image. The reflection image can have high-resolution, but it always be reconstructed using a pre-determined sound speed based on homogeneous assumption, and the mismatch between the pre-determined speed and real speeds of inhomogeneous tissue could result in phase aberration and degrade the image quality and resolution. In this article, we proposed and evaluated to correct phase aberration by bent-ray tracing method using sound speed image of USCT. First, sound speed image of USCT was reconstructed by first arrival picking method, bent-ray tracing method and Simultaneous Algebraic Reconstruction Technique (SART). Second, the propagation time from each element to each pixel of image was calculated by bent-ray tracing method using the sound speed image. Third, scatter-signal-delay-time was calculated and reflection image is reconstructed by using Synthetic Aperture (SA) method. For evaluation, both simulation and experiment validation were conducted. With regard to the simulation, the homogeneous point scatter simulation showed that the phase aberration might increase diameter of point-spread function (PSF) from approximate 1 to 5 mm; the complex-breast-model simulation demonstrated that the proposed method could correct phase aberration and decrease diameter of PSF from about 2 to 1 mm. With regard to the experiment, the results demonstrated that the proposed method could correct phase aberration and improve the image quality of breast phantom.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122359744","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

Fast shape matching and retrieval with dynamic angular partition descriptor 基于动态角分区描述符的快速形状匹配与检索

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261497

P. Shi, Yongkun Wang, Ying Hu, Yaohui Jin

引用次数: 0

Rapid preprocessing of FMCW GB-SAR echo based on FPGA 基于FPGA的FMCW GB-SAR回波快速预处理

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261516

H. Tan, Weiming Tian, Cheng Hu

引用次数: 1

The GVF measurement and flow regime study of gas-water flows by ERT sensor ERT传感器气水流场流场测量及流型研究

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261456

Yuchi Deng, Yinyan Liu, Yi Li

引用次数: 0