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1.发明授权System and method for the generation of attenuation correction maps from MR images 有权用于从MR图像生成衰减校正图的系统和方法公开(公告)号：US08218848B2
公开(公告)日：2012-07-10
申请号：US12506333
申请日：2009-07-21
IPC分类号： G06K9/00
CPC分类号： G06K9/342 , G06K2209/051 , G06T7/11 , G06T7/149 , G06T2207/10088 , G06T2207/30004
摘要： A method for generating a positron emission tomography (PET) attenuation correction map from magnetic resonance (MR) images includes segmenting a 3-dimensional (3D) magnetic resonance (MR) whole-body image of a patient into low-signal regions, fat regions, and soft tissue regions; classifying the low-signal regions as either lungs, bones, or air by identifying lungs, identifying an abdominal station, and identifying a lower body station; and generating an attenuation map from the segmentation result by replacing the segmentation labels with corresponding representative attenuation coefficients.
摘要翻译： 用于从磁共振（MR）图像生成正电子发射断层摄影（PET）衰减校正图的方法包括将患者的三维（3D）磁共振（MR）全身图像分为低信号区域，脂肪区域 和软组织区域; 通过识别肺，识别腹部站和识别下半身站将低信号区域分类为肺，骨骼或空气; 以及通过用对应的代表衰减系数代替分割标签，从分割结果生成衰减图。
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2.发明申请SYSTEM AND METHOD FOR THE GENERATION OF ATTENUATION CORRECTION MAPS FROM MR IMAGES 有权用于从MR图像生成衰减校正图的系统和方法公开(公告)号：US20100021034A1
公开(公告)日：2010-01-28
申请号：US12506333
申请日：2009-07-21
IPC分类号： G06K9/00
CPC分类号： G06K9/342 , G06K2209/051 , G06T7/11 , G06T7/149 , G06T2207/10088 , G06T2207/30004
摘要： A method for generating a positron emission tomography (PET) attenuation correction map from magnetic resonance (MR) images includes segmenting a 3-dimensional (3D) magnetic resonance (MR) whole-body image of a patient into low-signal regions, fat regions, and soft tissue regions; classifying the low-signal regions as either lungs, bones, or air by identifying lungs, identifying an abdominal station, and identifying a lower body station; and generating an attenuation map from the segmentation result by replacing the segmentation labels with corresponding representative attenuation coefficients.
摘要翻译： 用于从磁共振（MR）图像生成正电子发射断层摄影（PET）衰减校正图的方法包括将患者的三维（3D）磁共振（MR）全身图像分为低信号区域，脂肪区域 和软组织区域; 通过识别肺，识别腹部站和识别下半身站将低信号区域分类为肺，骨骼或空气; 以及通过用对应的代表衰减系数代替分割标签，从分割结果生成衰减图。
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3.发明申请SYSTEM AND METHOD FOR UNSUPERVISED DETECTION AND GLEASON GRADING OF PROSTATE CANCER WHOLE MOUNTS USING NIR FLUORSCENCE 有权使用近红外荧光检查的前列腺癌全部病例的不稳定检测和GLEASON分级的系统和方法公开(公告)号：US20090161928A1
公开(公告)日：2009-06-25
申请号：US12326135
申请日：2008-12-02
申请人： Ali Khamene , Tina Ehtiati , John V. Frangioni , Vida Kianzad , Fred S. Azar
发明人： Ali Khamene , Tina Ehtiati , John V. Frangioni , Vida Kianzad , Fred S. Azar
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/6277 , G06T7/66 , G06T2207/10056 , G06T2207/10064 , G06T2207/20076 , G06T2207/20152 , G06T2207/30024 , G06T2207/30081
摘要： A method for unsupervised classification of histological images of prostatic tissue includes providing histological image data obtained from a slide simultaneously co-stained with NIR fluorescent and Hematoxylin-and-Eosin (H&E) stains, segmenting prostate gland units in the image data, forming feature vectors by computing discriminating attributes of the segmented gland units, and using the feature vectors to train a multi-class classifier, where the classifier classifies prostatic tissue into benign, prostatic intraepithelial neoplasia (PIN), and Gleason scale adenocarcinoma grades 1 to 5 categories.
摘要翻译： 用于前列腺组织的组织学图像的无监督分类的方法包括提供从与NIR荧光和苏木精 - 曙红（H＆E）染色同时共染色的载玻片获得的组织学图像数据，在图像数据中分割前列腺单位，形成特征向量 通过计算分段腺体单位的区别属性，并使用特征向量来训练多类分类器，其中分类器将前列腺组织分类为良性前列腺上皮内瘤变（PIN）和1〜5级的格里森量表腺癌。
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4.发明授权System and method for unsupervised detection and gleason grading of prostate cancer whole mounts using NIR fluorscence 有权使用NIR荧光法进行前列腺癌整体无障碍检测和gleason分级的系统和方法公开(公告)号：US08139831B2
公开(公告)日：2012-03-20
申请号：US12326135
申请日：2008-12-02
申请人： Ali Khamene , Tina Ehtiati , John V. Frangioni , Vida Kianzad , Fred S. Azar
发明人： Ali Khamene , Tina Ehtiati , John V. Frangioni , Vida Kianzad , Fred S. Azar
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/6277 , G06T7/66 , G06T2207/10056 , G06T2207/10064 , G06T2207/20076 , G06T2207/20152 , G06T2207/30024 , G06T2207/30081
摘要： A method for unsupervised classification of histological images of prostatic tissue includes providing histological image data obtained from a slide simultaneously co-stained with NIR fluorescent and Hematoxylin-and-Eosin (H&E) stains, segmenting prostate gland units in the image data, forming feature vectors by computing discriminating attributes of the segmented gland units, and using the feature vectors to train a multi-class classifier, where the classifier classifies prostatic tissue into benign, prostatic intraepithelial neoplasia (PIN), and Gleason scale adenocarcinoma grades 1 to 5 categories.
摘要翻译： 用于前列腺组织的组织学图像的无监督分类的方法包括提供从与NIR荧光和苏木精 - 曙红（H＆E）染色同时共染色的载玻片获得的组织学图像数据，在图像数据中分割前列腺单位，形成特征向量 通过计算分段腺体单位的区别属性，并使用特征向量来训练多类分类器，其中分类器将前列腺组织分类为良性前列腺上皮内瘤变（PIN）和1〜5级的格里森量表腺癌。
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