文件名称:first-phase
介绍说明－－下载内容均来自于网络，请自行研究使用
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 fr a mework 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 fr a mework 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 fr a mework 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 fr a mework 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
...........\2.jpg
...........\2.tif
...........\aneesha ali.pdf.pdf
...........\bicubic.m
...........\doc.txt.txt
...........\eds.tif
...........\esd.tif
...........\fcnFirstOrderStatisticsFilter.m
...........\gaussiankernel.m
...........\start.m
...........\test.m