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1. 发明申请

US20080120077A1 Method for kinetic characterization from temporal image sequence 审中-公开
标题翻译：从时间图像序列的动力学表征方法
公开(公告)号：US20080120077A1
公开(公告)日：2008-05-22
申请号：US11604590
申请日：2006-11-22
申请人： Shih-Jong J. Lee , Seho Oh , Yuhui Y.C. Cheng , Samuel V. Alworth
发明人： Shih-Jong J. Lee , Seho Oh , Yuhui Y.C. Cheng , Samuel V. Alworth
IPC分类号： G06G7/48
CPC分类号： G06K9/00127 , G06K2009/3291
摘要： A computerized derivable kinetic characterization measurement method for live cell kinetic characterization inputs kinetic recognition data for a plurality of time frames. A single cell measurement step is performed using the kinetic recognition data for a plurality of time frames to generate single cell feature for a plurality of time frames output. The single cell feature includes cell morphological profiling feature. A kinetic measurement step uses the single cell feature for a plurality of time frames to generate kinetic feature output. A trajectory measurement step uses the single cell feature for a plurality of time frames and the kinetic feature to generate trajectory feature output. An interval measurement step uses the kinetic feature to generate interval feature output. A cell state classifier step uses the interval feature to generate cell state output. A state based measurement uses the single cell feature, the kinetic feature and the cell state to generate state based feature output.
摘要翻译：用于活细胞动力学特征的计算机可推导动力学表征测量方法输入多个时间帧的动力学识别数据。使用多个时间帧的动力学识别数据来执行单个小区测量步骤，以生成多个时间帧输出的单个小区特征。单细胞特征包括细胞形态分析特征。动力学测量步骤使用多个时间帧的单细胞特征来产生动力特征输出。轨迹测量步骤使用单个小区特征用于多个时间帧，并且所述动力特征生成轨迹特征输出。间隔测量步骤使用动力学特征来产生间隔特征输出。单元状态分类器步骤使用间隔特征来生成单元状态输出。基于状态的测量使用单细胞特征，动力学特征和细胞状态来产生基于状态的特征输出。

2. 发明申请

US20080044084A1 Integrated human-computer interface for image recognition 有权
标题翻译：集成人机界面，用于图像识别
公开(公告)号：US20080044084A1
公开(公告)日：2008-02-21
申请号：US11506081
申请日：2006-08-16
申请人： Shih-Jong J. Lee , Samuel V. Alworth , Tuan Phan , Chi Chou Huang , Christopher Birnbaum
发明人： Shih-Jong J. Lee , Samuel V. Alworth , Tuan Phan , Chi Chou Huang , Christopher Birnbaum
IPC分类号： G06K9/46
CPC分类号： G06T5/00 , G06T7/00 , G06T2200/24 , G06T2207/30004
摘要： A Recognition Frame presents multi-level application elements to the user simultaneously through a computer graphical user interface. The interface consists of an image display panel for displaying image channels; a data display panel for displaying object measurements and summary statistics; a configuration display panel for displaying recipe content; a master tab for selecting the panels. It also consists of a processing toolbar for context dependent processing tool display. The Recognition Frame further comprises a second side frame for data object display and charting. The second side frame has a tabular arrangement consisting of properties tab, controls tab, and charts tab. The Recognition Frame links application elements through a complex data model wherein interface display is automatically updated when one element is changed.
摘要翻译：识别框架通过计算机图形用户界面同时向用户提供多层应用程序元素。该接口包括用于显示图像通道的图像显示面板; 用于显示物体测量和总结统计数据的数据显示面板; 用于显示配方内容的配置显示面板; 用于选择面板的主选项卡。它还包括用于上下文相关处理工具显示的处理工具栏。识别框架还包括用于数据对象显示和制图的第二侧框架。第二侧框架具有由属性选项卡，控件选项卡和图表选项卡组成的表格布置。识别框架通过复杂数据模型来链接应用元素，其中当一个元素被改变时，自动更新界面显示。

3. 发明申请

US20070297675A1 Method of directed feature development for image pattern recognition 审中-公开
标题翻译：图像模式识别的定向特征开发方法
公开(公告)号：US20070297675A1
公开(公告)日：2007-12-27
申请号：US11475644
申请日：2006-06-26
申请人： Shih-Jong J. Lee , Seho Oh
发明人： Shih-Jong J. Lee , Seho Oh
IPC分类号： G06K9/62 , G06K9/46 , G06K9/66
CPC分类号： G06K9/623 , G06K9/6254
摘要： A computerized directed feature development method receives an initial feature list, a learning image and object masks. Interactive feature enhancement is performed by human to generate feature recipe. The Interactive feature enhancement includes a visual profiling selection method and a contrast boosting method.A visual profiling selection method for computerized directed feature development receives initial feature list, initial features, learning image and object masks. Information measurement is performed to generate information scores. Ranking of the initial feature list is performed to generate a ranked feature list. Human selection is performed through a user interface to generate a profiling feature. A contrast boosting feature optimization method performs extreme example specification by human to generate updated montage. Extreme directed feature ranking is performed to generate extreme ranked features. Contrast boosting feature generation is performed to generate new features and new feature generation rules.
摘要翻译：计算机定向特征开发方法接收初始特征列表，学习图像和对象掩模。交互功能增强是由人执行以产生特征配方。交互功能增强功能包括视觉分析选择方法和对比度增强方法。用于计算机定向特征开发的视觉分析选择方法接收初始特征列表，初始特征，学习图像和对象掩码。执行信息测量以产生信息得分。执行初始特征列表的排名以生成排名特征列表。通过用户界面执行人工选择以生成分析特征。对比度提升特征优化方法由人类执行极端示例规范以生成更新的蒙太奇。执行极端定向特征排名以产生极端排名的特征。执行对比度增强特征生成以生成新特征和新特征生成规则。

4. 发明申请

US20060072817A1 Method for robust analysis of biological activity in microscopy images 有权
公开(公告)号：US20060072817A1
公开(公告)日：2006-04-06
申请号：US10952579
申请日：2004-09-29
申请人： Shih-Jong Lee , Samuel Alworth
发明人： Shih-Jong Lee , Samuel Alworth
IPC分类号： G06K9/34
CPC分类号： G06K9/00127
摘要： A robust object segmentation method for analysis of biological activity receives an input image and performs segmentation confidence mapping using the input image to generate segmentation confidence map output. A thresholding is performed using the object segmentation confidence map to generate a high confidence object mask output. An object segmentation confidence mapping method for analysis of biological activity receives an input image and performs segmentation decision to create segmentation decision result. A difference operation is performed to generate the segmentation decision result. A confidence mapping is performed using the difference result to generate segmentation confidence. An object level robust analysis method for biological activity receives an input image and performs object segmentation using the input image to create object segmentation result. A robust object feature measurement is performed to generate robust object feature result. An FOV level robust analysis method for biological activity receives a plurality of object feature results and performs robust FOV summary feature extraction to create robust FOV summary features. A FOV regulated feature extraction is performed to generate FOV regulated features. A FOV regulated feature extraction method for biological activity receives a plurality of object feature results and performs control object selection using the plurality of object feature results to generate control objects output. A FOV regulated feature extraction is performed to generate FOV regulation features output. An object feature FOV regulation is performed using the plurality of object feature results and the FOV regulation features to generate FOV regulated object features output. A sample level robust analysis method for biological activity receives a plurality of FOV feature results and performs robust sample summary feature extraction to create robust sample summary features. A sample regulated feature extraction is performed to generate sample regulated features. An assay level robust analysis method for biological activity receives a plurality of sample feature results and performs robust assay summary feature extraction to create robust assay summary features. An assay regulated feature extraction is performed to generate assay regulated features.

5. 发明申请

US20080112606A1 Method for moving cell detection from temporal image sequence model estimation 有权
标题翻译：从时间图像序列模型估计中移动细胞检测的方法
公开(公告)号：US20080112606A1
公开(公告)日：2008-05-15
申请号：US11595611
申请日：2006-11-09
申请人： Shih-Jong J. Lee , Seho Oh
发明人： Shih-Jong J. Lee , Seho Oh
IPC分类号： G06K9/00
CPC分类号： G06K9/0014 , G06T7/254 , G06T2207/10056 , G06T2207/30024
摘要： A computerized robust cell kinetic recognition method for moving cell detection from temporal image sequence receives an image sequence containing a current image. A dynamic spatial-temporal reference generation is performed to generate dynamic reference image output. A reference based object segmentation is performed to generate initial object segmentation output. An object matching and detection refinement is performed to generate kinetic recognition results output. The dynamic spatial-temporal reference generation step performs frame look ahead and the reference images contain a reference intensity image and at least one reference variation image.
摘要翻译：用于从时间图像序列移动小区检测的计算机化的鲁棒小区动力学识别方法接收包含当前图像的图像序列。执行动态空间 - 时间参考生成以生成动态参考图像输出。执行基于参考的对象分割以产生初始对象分割输出。执行物体匹配和检测细化以产生动力学识别结果输出。动态空间 - 时间参考生成步骤执行帧前瞻，并且参考图像包含参考强度图像和至少一个参考变化图像。

6. 发明申请

US20060285743A1 Object based boundary refinement method 有权
标题翻译：基于对象的边界细化方法
公开(公告)号：US20060285743A1
公开(公告)日：2006-12-21
申请号：US11165561
申请日：2005-06-20
申请人： Seho Oh , Shih-Jong Lee
发明人： Seho Oh , Shih-Jong Lee
IPC分类号： G06K9/00 , G06K9/48
CPC分类号： G06K9/0014 , G06K9/34 , G06T7/0012 , G06T7/12 , G06T7/155 , G06T2207/20104 , G06T2207/20192 , G06T2207/30024
摘要： An object based boundary refinement method for object segmentation in digital images receives an image and a single initial object region of interest and performs refinement zone definition using the initial object regions of interest to generate refinement zones output. A directional edge enhancement is performed using the input image and the refinement zones to generate directional enhanced region of interest output. A radial detection is performed using the input image the refinement zones and the directional enhanced region of interest to generate radial detection mask output. In addition, a final shaping is performed using the radial detection mask having single object region output. A directional edge enhancement method determining pixel specific edge contrast enhancement direction according to the object structure direction near the pixel consists receives an image and refinement zones and performs 1D horizontal distance transform and 1D vertical distance transform using the refinement zones to generate horizontal distance map and vertical distance map outputs. A neighboring direction determination is performed using the horizontal distance map and the vertical distance map to generate neighboring image output. In addition, a directional edge contrast calculation using the neighboring image and input image having directional enhanced region of interest output.
摘要翻译：用于数字图像中对象分割的基于对象的边界细化方法接收图像和感兴趣的单个初始对象区域，并使用感兴趣的初始对象区域执行细化区域定义，以生成细化区域输出。使用输入图像和细化区域来执行方向边缘增强以产生方向增强的兴趣区域输出。使用输入图像进行径向检测，该细化区域和方向增强区域用于产生径向检测掩模输出。另外，使用具有单个物体区域输出的径向检测掩模进行最终成形。根据像素附近的物体结构方向确定像素特征边缘对比度增强方向的方向边缘增强方法包括接收图像和细化区域，并使用细化区域进行1D水平距离变换和1D垂直距离变换，以生成水平距离图和垂直距离图输出。使用水平距离图和垂直距离图执行相邻方向确定以生成相邻图像输出。另外，使用相邻图像的方向边缘对比度计算和具有方向增强感兴趣区域输出的输入图像。

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