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公开(公告)号：US20110301447A1
公开(公告)日：2011-12-08
申请号：US13134507
申请日：2011-06-07
申请人： Sun Young Park , Dustin Sargent , Ulf Peter Gustafsson , Wenjing Li , Rolf Wolters , Stephen D. Fleischer
发明人： Sun Young Park , Dustin Sargent , Ulf Peter Gustafsson , Wenjing Li , Rolf Wolters , Stephen D. Fleischer
CPC分类号： G06T7/0016 , G06T2207/10016 , G06T2207/10068 , G06T2207/20076 , G06T2207/20081 , G06T2207/30032
摘要： A process and device for detecting colon cancer by classifying and annotating clinical features in video data containing colonoscopic features by applying a probabilistic analysis to intra-frame and inter-frame relationships between colonoscopic features in spatially and temporally neighboring portions of video frames, and classifying and annotating as clinical features any of the colonoscopic features that satisfy the probabilistic analysis as clinical features. Preferably the probabilistic analysis is Hidden Markove Model analysis, and the process is carried out by a computer trained using semi supervised learning from labeled and unlabeled examples of clinical features in video containing colonoscopic features.
摘要翻译： 通过对视频帧的空间和时间相邻部分中的结肠镜检查特征之间的帧内和帧间关系进行概率分析，对包含结肠镜特征的视频数据中的临床特征进行分类和注释来检测结肠癌的过程和装置，以及分类和 注释为临床特征任何结肠镜检查特征，满足作为临床特征的概率分析。 优选地，概率分析是隐藏的Markove模型分析，并且该过程由使用半监督学习的计算机进行，该计算机通过标签和未标记的包含结肠镜特征的视频的临床特征实例来实施。
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公开(公告)号：US08503747B2
公开(公告)日：2013-08-06
申请号：US13068188
申请日：2011-05-03
CPC分类号： G06K9/6284 , A61B5/4331 , G06K9/6297 , G06T7/0012 , G06T2207/10012 , G06T2207/10016 , G06T2207/10024 , G06T2207/20076 , G06T2207/30096
摘要： The present invention is an automated image analysis framework for cervical cancerous lesion detection. The present invention uses domain-specific diagnostic features in a probabilistic manner using conditional random fields. In addition, the present invention discloses a novel window-based performance assessment scheme for two-dimensional image analysis, which addresses the intrinsic problem of image misalignment. As a domain-specific anatomical feature, image regions corresponding to different tissue types are extracted from cervical images taken before and after the application of acetic acid during a clinical exam. The unique optical properties of each tissue type and the diagnostic relationships between neighboring regions are incorporated in the conditional random field model. The output provides information about both the tissue severity and the location of cancerous tissue in an image.
摘要翻译： 本发明是用于宫颈癌性病变检测的自动图像分析框架。 本发明以概率方式使用条件随机场来使用特定领域的诊断特征。 另外，本发明公开了一种用于二维图像分析的新颖的基于窗口的性能评估方案，其解决了图像未对准的固有问题。 作为区域特异性解剖特征，在临床检查期间，在应用乙酸之前和之后从宫颈图像中提取对应于不同组织类型的图像区域。 每个组织类型的独特光学性质和相邻区域之间的诊断关系被并入条件随机场模型中。 输出提供关于组织严重性和图像中癌组织位置的信息。
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公开(公告)号：US20110274338A1
公开(公告)日：2011-11-10
申请号：US13068188
申请日：2011-05-03
IPC分类号： G06K9/00
CPC分类号： G06K9/6284 , A61B5/4331 , G06K9/6297 , G06T7/0012 , G06T2207/10012 , G06T2207/10016 , G06T2207/10024 , G06T2207/20076 , G06T2207/30096
摘要： The present invention is an automated image analysis framework for cervical cancerous lesion detection. The present invention uses domain-specific diagnostic features in a probabilistic manner using conditional random fields. In addition, the present invention discloses a novel window-based performance assessment scheme for two-dimensional image analysis, which addresses the intrinsic problem of image misalignment. As a domain-specific anatomical feature, image regions corresponding to different tissue types are extracted from cervical images taken before and after the application of acetic acid during a clinical exam. The unique optical properties of each tissue type and the diagnostic relationships between neighboring regions are incorporated in the conditional random field model. The output provides information about both the tissue severity and the location of cancerous tissue in an image.
摘要翻译： 本发明是用于宫颈癌性病变检测的自动图像分析框架。 本发明以概率方式使用条件随机场来使用特定领域的诊断特征。 另外，本发明公开了一种用于二维图像分析的新颖的基于窗口的性能评估方案，其解决了图像未对准的固有问题。 作为区域特异性解剖特征，在临床检查期间，在应用乙酸之前和之后从宫颈图像中提取对应于不同组织类型的图像区域。 每个组织类型的独特光学性质和相邻区域之间的诊断关系被并入条件随机场模型中。 输出提供关于组织严重性和图像中癌组织位置的信息。
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