公开(公告)号：US5799101A

公开(公告)日：1998-08-25

申请号：US897392

申请日：1997-07-21

申请人： Shih-Jong J. Lee ,  Seho Oh ,  Stanley F. Patten ,  Alan C. Nelson ,  Larry A. Nelson

发明人： Shih-Jong J. Lee ,  Seho Oh ,  Stanley F. Patten ,  Alan C. Nelson ,  Larry A. Nelson

IPC分类号： G06K9/00 ,  G06K9/68 ,  G01N33/48

CPC分类号： G06K9/6282 ,  G06K9/00127 ,  Y10S128/922

摘要： A computer aided biological specimen screener is responsive to a priori information about biological specimens. An automated slide classifier scans a slide specimen and generates a set of scores. The classification which results from this set of scores depends on a priori information concerning the patient, such as patient age and prior health history and diagnosis. Using the patient information and an appropriate score, the specimen is classified. A normal threshold used for classification is changed over a wide range depending on the specificity appropriate to a particular patient risk category. The sensitivity of the classification is automatically maximized for a predetermined normal specificity.

摘要翻译： 计算机辅助生物样本筛选器响应关于生物样本的先验信息。 自动滑块分类器扫描幻灯片样本并生成一组分数。 由该分数得出的分类取决于患者的先验信息，例如患者年龄和以前的健康史和诊断。 使用患者信息和适当的分数，将样本分类。 用于分类的正常阈值根据对特定患者风险类别的特异性而在宽范围内变化。 对于预定的正常特异性，分类的灵敏度自动最大化。

公开(公告)号：US06842538B2

公开(公告)日：2005-01-11

申请号：US09815816

申请日：2001-03-23

申请人： Shih-Jong J. Lee ,  Seho Oh ,  Larry A. Nelson

发明人： Shih-Jong J. Lee ,  Seho Oh ,  Larry A. Nelson

IPC分类号： G06K9/46 ,  G06K9/00 ,  G06K9/36 ,  G06K9/62 ,  H04N1/00

CPC分类号： G06K9/4604

摘要： Mark detection and position determination are improved by use of directional elongated filters, symmetry, gray scale image processing, structural constraints, and learning. Directional elongated filters are used to pre-process images of registration marks to create masks and enhanced images. Working sequentially, portions of the mark are detected and classified. The input gray scale image of the mark is processed using its structural constraints in conjunction with a mask for the detected mark. A cost function estimation determines mark position and orientation with sub-pixel accuracy. Learning is used to improve specific application performance.

摘要翻译： 通过使用定向细长滤波器，对称性，灰度图像处理，结构约束和学习来提高标记检测和位置确定。 定向拉长过滤器用于预处理对准标记的图像以创建掩模和增强图像。 按顺序工作，对标记的部分进行检测和分类。 使用其结构约束结合用于检测标记的掩码来处理标记的输入灰度图像。 成本函数估计用子像素精度确定标记位置和方向。 学习用于提高特定的应用程序性能。

公开(公告)号：US06181811B2

公开(公告)日：2001-01-30

申请号：US09006457

申请日：1998-01-13

申请人： Chih-Chau L. Kuan ,  Shih-Jong J. Lee ,  Seho Oh ,  Larry A. Nelson ,  Dayle G. Ellison ,  Florence W. Patten

发明人： Chih-Chau L. Kuan ,  Shih-Jong J. Lee ,  Seho Oh ,  Larry A. Nelson ,  Dayle G. Ellison ,  Florence W. Patten

IPC分类号： G06K900

CPC分类号： G06K9/00127

摘要： A method and apparatus for optimizing biological and cytological specimen screening and diagnosis. A slide review process is recommended for cytological specimen screening to identify abnormal sub-populations for further review and also diagnosis by a human expert. An automated screener processes a cytological specimen. Using a slide score generated by the automated screener, a slide review process using a slide score classification is determined. The recommendation of slide review processes improves overall performance of the screening process as measured by sensitivity to abnormal specimens, and at the same time reduces the work load of a human reviewer. The system also effectively and smoothly integrates the process of initial screening of the specimen with the process of further review of the specimen and final diagnosis of the specimen.

摘要翻译： 用于优化生物学和细胞学标本筛选和诊断的方法和装置。 推荐用于细胞学标本筛选的幻灯片审查过程，以识别异常亚群以供进一步审查，并由人类专家进行诊断。 自动筛选器处理细胞学标本。 使用由自动筛选器生成的幻灯片得分，确定使用幻灯片分数分类的幻灯片审阅过程。 幻灯片审查过程的推荐通过对异常标本的敏感度测量，提高了筛选过程的整体性能，同时降低了人类审阅者的工作量。 该系统还将样品初始筛选过程与样品进一步检查和样品的最终诊断过程进行了有效平滑的整合。

公开(公告)号：US5627908A

公开(公告)日：1997-05-06

申请号：US309063

申请日：1994-09-20

申请人： Shih-Jong J. Lee ,  Alan C. Nelson ,  Larry A. Nelson ,  Carl E. Youngmann ,  Keith L. Frost

发明人： Shih-Jong J. Lee ,  Alan C. Nelson ,  Larry A. Nelson ,  Carl E. Youngmann ,  Keith L. Frost

IPC分类号： G01N15/14 ,  G06K9/00 ,  G06K9/38

CPC分类号： G01N15/1475

摘要： A cytological system dynamic normalization of a normal threshold. An analysis score from a slide is compared against a threshold to determine whether or not the slide is normal or requires microscopy review. The normal threshold is dynamically adjusted using a three step process. The process is implemented on an automatic cytology system. The first step is initial calibration of the system to determine an initial threshold. The second step is a running adjustment of the normal threshold in response to the presentation of new slides to the automatic cytology system. The third step is the batch certification of every slide. The threshold may be adjusted for an analysis score, a quality control score, or a screening score.

摘要翻译： 细胞学系统动态归一化正常阈值。 将幻灯片的分析得分与阈值进行比较，以确定幻灯片是否正常或需要显微镜检查。 使用三步过程动态调整正常阈值。 该过程在自动细胞学系统上实现。 第一步是系统的初始校准，以确定初始阈值。 第二步是响应于向自动细胞学系统呈现新幻灯片而正常阈值的运行调整。 第三步是每张幻灯片的批量认证。 可以针对分析得分，质量控制评分或筛选分数调整阈值。

公开(公告)号：US07263509B2

公开(公告)日：2007-08-28

申请号：US10411437

申请日：2003-04-09

申请人： Shih-Jong J. Lee ,  Seho Oh

发明人： Shih-Jong J. Lee ,  Seho Oh

IPC分类号： G06F12/00 ,  G06N5/02

CPC分类号： G06N5/04 ,  G06K9/342 ,  G06K9/4638 ,  G06N5/025 ,  G06N99/005

摘要： An intelligent spatial reasoning method receives a plurality of object sets. A spatial mapping feature learning method uses the plurality of object sets to create at least one salient spatial mapping feature output. It performs spatial reasoning rule learning using the at least one spatial mapping feature to create at least one spatial reasoning rule output. The spatial mapping feature learning method performs a spatial mapping feature set generation step followed by a feature learning step. The spatial mapping feature set is generated by repeated application of spatial correlation between two object sets. The feature learning method consists of a feature selection step and a feature transformation step and the spatial reasoning rule learning method uses the supervised learning method.The spatial reasoning approach of this invention automatically characterizes spatial relations of multiple sets of objects by comprehensive collections of spatial mapping features. Some of the features have clearly understandable physical, structural, or geometrical meanings. Others are statistical characterizations, which may not have clear physical, structural or geometrical meanings when considered individually. A combination of these features, however, could characterize subtle physical, structural or geometrical conditions under practical situations. One key advantage of this invention is the ability to characterize subtle differences numerically using a comprehensive feature set.

摘要翻译： 智能空间推理方法接收多个对象集。 空间映射特征学习方法使用多个对象集来创建至少一个显着的空间映射特征输出。 它使用至少一个空间映射特征来执行空间推理规则学习以创建至少一个空间推理规则输出。 空间映射特征学习方法执行空间映射特征集生成步骤，随后是特征学习步骤。 通过重复应用两个对象集之间的空间相关性来生成空间映射特征集。 特征学习方法由特征选择步骤和特征变换步骤组成，空间推理规则学习方法采用监督学习方法。 本发明的空间推理方法通过空间映射特征的综合集合自动表征多组对象的空间关系。 一些功能具有明确的理解，物理，结构或几何意义。 其他是统计特征，当单独考虑时可能没有明确的物理，结构或几何意义。 然而，这些特征的组合可以在实际情况下表征微妙的物理，结构或几何条件。 本发明的一个关键优点是能够使用综合特征集在数值上表征微妙的差异。

公开(公告)号：US07076093B2

公开(公告)日：2006-07-11

申请号：US10247723

申请日：2002-09-16

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： G06T7/001 ,  G06T7/12 ,  G06T7/155 ,  G06T2207/20041

摘要： A structure-guided transformation transforms a region of an image into a region in the structure-transformed image according to the desired structure. The invention achieves efficient and accurate structure-guided processing such as filtering, detection and comparison in the transformed domain and thereby facilitates use of simple operations to enhance or detect straight lines or edges. Structure information is used to enhance and detect image features of interest even when the shape of the image structure is not regular. Both global and local structures of objects can be inspected. Global structure inspection detects gross errors in image structure; therefore side effects caused by mismatched structure-guided processing are avoided. Subtle defects along the edge of a structure can be detected by local structure inspection. Structure information guidance provides an edge detection inspection system that tolerates significant noise and contrast variations.

摘要翻译： 结构引导变换根据期望的结构将图像的区域变换为结构变换图像中的区域。 本发明实现了有效和准确的结构引导处理，如变换域中的过滤，检测和比较，从而便于使用简单的操作来增强或检测直线或边缘。 即使当图像结构的形状不规则时，结构信息也用于增强和检测感兴趣的图像特征。 可以检查对象的全局和局部结构。 全局结构检查检测图像结构中的粗略错误; 因此避免了由错配的结构引导处理引起的副作用。 沿结构边缘的微小缺陷可以通过局部结构检查来检测。 结构信息指导提供了边缘检测检测系统，其容忍显着的噪声和对比度变化。

公开(公告)号：US06404934B1

公开(公告)日：2002-06-11

申请号：US09692948

申请日：2000-10-20

申请人： Shih-Jong J. Lee ,  Seho Oh

发明人： Shih-Jong J. Lee ,  Seho Oh

IPC分类号： G06T520

CPC分类号： G06T5/30

摘要： A high speed image processing apparatus is created through the use of cascaded elongated filters. The processing speed of the filters is kernel size insensitive, enabling use of general purpose computing facilities to process high resolution, monochrome, and multi-spectrum images. Elongated filters described include both linear and non-linear filters. Very large kernel and multi-dimensional image processing is accomplished with reduced complexity and portable programming instructions.

摘要翻译： 通过使用级联的细长过滤器创建高速图像处理装置。 过滤器的处理速度是内核大小不敏感的，可以使用通用计算机来处理高分辨率，单色和多光谱图像。 所描述的细长滤波器包括线性和非线性滤波器。 非常大的内核和多维图像处理通过降低的复杂性和便携式编程指令来实现。

公开(公告)号：US6134354A

公开(公告)日：2000-10-17

申请号：US121012

申请日：1998-07-22

申请人： Shih-Jong J. Lee ,  Paul S. Wilhelm ,  Wendy R. Bannister ,  Chih-Chau L. Kuan ,  Seho Oh ,  Michael G. Meyer

发明人： Shih-Jong J. Lee ,  Paul S. Wilhelm ,  Wendy R. Bannister ,  Chih-Chau L. Kuan ,  Seho Oh ,  Michael G. Meyer

IPC分类号： G01N15/14 ,  G06K9/00 ,  G06K9/38

CPC分类号： G06K9/00127 ,  G01N15/1475 ,  G01N2015/1497

摘要： A free-lying cell classifier. An automated microscope system comprising a computer and high speed processing field of view processors identifies free-lying cells. An image of a biological specimen is obtained and the image is segmented to create a set of binary masks. The binary masks are used by a feature calculator to compute the features that characterize objects of interest including free-lying cells, artifacts and other biological objects. The objects are classified to identify their type, their normality or abnormality or their identification as an artifact. The results are summarized and reported. A stain evaluation of the slide is performed as well as a typicality evaluation. The robustness of the measurement is also quantified as a classification confidence value. The free-lying cell evaluation is used by an automated cytology system to classify a biological specimen slide.

公开(公告)号：US20120106809A1

公开(公告)日：2012-05-03

申请号：US12925874

申请日：2010-11-01

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： G06K9/342 ,  G06K9/0014

摘要： A teachable object contour mapping method for region partition receives an object boundary and a teaching image. An object contour mapping recipe creation is performed using the object boundary and the teaching image to generate object contour mapping recipe output. An object contour mapping is applied to an application image using the object contour mapping recipe and the application image to generate object contour map output. An object region partition using the object contour map to generate object region partition output An updateable object contour mapping method receives a contour mapping recipe and a validation image. An object contour mapping is performed using the object contour mapping recipe and the validation image to generate validation contour map output. An object region partition receives a region mask to generate validation object region partition output. A boundary correction is performed using the validation object region partition to generate corrected object boundary output. An update contour mapping is performed using the corrected object boundary, the validation image and the contour mapping recipe to generate updated contour mapping recipe output.

摘要翻译： 区域分区的可教对象轮廓映射方法接收对象边界和教学图像。 使用对象边界和教学图像执行对象轮廓映射配方创建，以生成对象轮廓映射配方输出。 使用对象轮廓映射配方和应用图像将对象轮廓映射应用于应用图像以生成对象轮廓图输出。 使用对象轮廓图生成对象区域分区输出的对象区域分区可更新对象轮廓映射方法接收轮廓映射配方和验证图像。 使用对象轮廓映射配方和验证图像执行对象轮廓映射以生成验证轮廓图输出。 对象区域分区接收区域掩码以生成验证对象区域分区输出。 使用验证对象区域分区执行边界校正，以生成校正对象边界输出。 使用校正的对象边界，验证图像和轮廓映射配方来执行更新轮廓映射以生成更新的轮廓映射配方输出。

公开(公告)号：US20110274339A1

公开(公告)日：2011-11-10

申请号：US13135711

申请日：2011-07-13

申请人： Shih-Jong J. Lee ,  Seho Oh ,  Samuel V. Alworth

发明人： Shih-Jong J. Lee ,  Seho Oh ,  Samuel V. Alworth

IPC分类号： G06K9/00

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格状态输出。 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。