文件名称:first-phase
介绍说明－－下载内容来自于网络，使用问题请自行百度
The aim of this paper is to describe a novel and completely
automated technique for carotid artery (CA) recognition,
far (distal) wall segmentation, and intima–media thickness (IMT)
measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale–space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen–intima-The aim of this paper is to describe a novel and completely
automated technique for carotid artery (CA) recognition,
far (distal) wall segmentation, and intima–media thickness (IMT)
measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale–space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen–intima
automated technique for carotid artery (CA) recognition,
far (distal) wall segmentation, and intima–media thickness (IMT)
measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale–space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen–intima-The aim of this paper is to describe a novel and completely
automated technique for carotid artery (CA) recognition,
far (distal) wall segmentation, and intima–media thickness (IMT)
measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale–space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen–intima
(系统自动生成,下载前可以参看下载内容)
下载文件列表
first phase/
first phase/2.jpg
first phase/2.tif
first phase/aneesha ali.pdf.pdf
first phase/bicubic.m
first phase/doc.txt.txt
first phase/eds.tif
first phase/esd.tif
first phase/fcnFirstOrderStatisticsFilter.m
first phase/gaussiankernel.m
first phase/start.m
first phase/test.m
first phase/2.jpg
first phase/2.tif
first phase/aneesha ali.pdf.pdf
first phase/bicubic.m
first phase/doc.txt.txt
first phase/eds.tif
first phase/esd.tif
first phase/fcnFirstOrderStatisticsFilter.m
first phase/gaussiankernel.m
first phase/start.m
first phase/test.m
本网站为编程资源及源代码搜集、介绍的搜索网站，版权归原作者所有！ 粤ICP备11031372号
1999-2046 搜珍网 All Rights Reserved.