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公开(公告)号:US08781192B2
公开(公告)日:2014-07-15
申请号:US13702136
申请日:2011-04-27
申请人: Yechiel Lamash , Jonathan Lessick , Asher Gringauz
发明人: Yechiel Lamash , Jonathan Lessick , Asher Gringauz
CPC分类号: G06K9/00536 , G06K9/4647 , G06K2209/05 , G06T7/0012 , G06T7/41 , G06T2207/10072 , G06T2207/30048
摘要: A method for classifying tissue as normal or abnormal tissue includes obtaining segmented reconstructed volumetric image data for predetermined tissue of interest, generating a 2D voxel representation of the segmented reconstructed volumetric image data, and classifying voxels of the segmented reconstructed volumetric image data as corresponding to abnormal and normal tissue based on the 2D voxel representation.
摘要翻译: 将组织分类为正常或异常组织的方法包括获得预定组织的分段重建体积图像数据,生成分割重建体积图像数据的2D体素表示,并将分割的重建体积图像数据的体素分类为对应于异常 和基于2D体素表示的正常组织。
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公开(公告)号:US20130077838A1
公开(公告)日:2013-03-28
申请号:US13702136
申请日:2011-04-27
申请人: Yechiel Lamash , Jonathan Lessick , Asher Gringauz
发明人: Yechiel Lamash , Jonathan Lessick , Asher Gringauz
IPC分类号: G06K9/00
CPC分类号: G06K9/00536 , G06K9/4647 , G06K2209/05 , G06T7/0012 , G06T7/41 , G06T2207/10072 , G06T2207/30048
摘要: A method for classifying tissue as normal or abnormal tissue includes obtaining segmented reconstructed volumetric image data for predetermined tissue of interest, generating a 2D voxel representation of the segmented reconstructed volumetric image data, and classifying voxels of the segmented reconstructed volumetric image data as corresponding to abnormal and normal tissue based on the 2D voxel representation.
摘要翻译: 将组织分类为正常或异常组织的方法包括获得预定组织的分段重建体积图像数据,生成分割重建体积图像数据的2D体素表示,并将分割的重建体积图像数据的体素分类为对应于异常 和基于2D体素表示的正常组织。
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公开(公告)号:US20130243348A1
公开(公告)日:2013-09-19
申请号:US13989158
申请日:2011-11-16
申请人: Liran Goshen , Asher Gringauz , Yechiel Lamash , Andrei Feldman , Guido Pardo-Roques , Jonathan Sapir
发明人: Liran Goshen , Asher Gringauz , Yechiel Lamash , Andrei Feldman , Guido Pardo-Roques , Jonathan Sapir
IPC分类号: G06T5/00
摘要: A method includes enhancing a contrast to noise ratio (CNR) of image data, generating CNR enhanced image data, wherein the CNR enhanced image data has a substantially same image quality as the image data. A computing system (118) includes a computer readable storage medium (122) encoded with computer readable instructions for enhancing a contrast to noise ratio (CNR) of image data and one or more processors (120), which, when executing the computer readable instructions, causes the computing system to enhance the CNR of the image data. A method includes generating CNR enhanced image data, wherein CNR enhanced image data has a substantially same noise level, noise power spectrum, and spatial resolution of the image data.
摘要翻译: 一种方法包括提高图像数据的对比度(CNR),生成CNR增强图像数据,其中CNR增强图像数据具有与图像数据基本相同的图像质量。 计算系统(118)包括用计算机可读指令编码的用于增强图像数据的对比度(CNR)和一个或多个处理器(120)的计算机可读存储介质(122),当执行计算机可读指令时 ,使计算系统增强图像数据的CNR。 一种方法包括生成CNR增强图像数据,其中CNR增强图像数据具有基本相同的噪声水平,噪声功率谱和图像数据的空间分辨率。
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公开(公告)号:US09159124B2
公开(公告)日:2015-10-13
申请号:US13989158
申请日:2011-11-16
申请人: Liran Goshen , Asher Gringauz , Yechiel Lamash , Andrei Feldman , Guido Pardo-Roques , Jonathan Sapir
发明人: Liran Goshen , Asher Gringauz , Yechiel Lamash , Andrei Feldman , Guido Pardo-Roques , Jonathan Sapir
摘要: A method includes enhancing a contrast to noise ratio (CNR) of image data, generating CNR enhanced image data, wherein the CNR enhanced image data has a substantially same image quality as the image data. A computing system (118) includes a computer readable storage medium (122) encoded with computer readable instructions for enhancing a contrast to noise ratio (CNR) of image data and one or more processors (120), which, when executing the computer readable instructions, causes the computing system to enhance the CNR of the image data. A method includes generating CNR enhanced image data, wherein CNR enhanced image data has a substantially same noise level, noise power spectrum, and spatial resolution of the image data.
摘要翻译: 一种方法包括提高图像数据的对比度(CNR),生成CNR增强图像数据,其中CNR增强图像数据具有与图像数据基本相同的图像质量。 计算系统(118)包括用计算机可读指令编码的用于增强图像数据的对比度(CNR)和一个或多个处理器(120)的计算机可读存储介质(122),当执行计算机可读指令时 ,使计算系统增强图像数据的CNR。 一种方法包括生成CNR增强图像数据,其中CNR增强图像数据具有基本相同的噪声水平,噪声功率谱和图像数据的空间分辨率。
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