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1. 发明授权

US08165369B2 System and method for robust segmentation of pulmonary nodules of various densities 有权
标题翻译：用于各种密度的肺结节的鲁棒分割的系统和方法
公开(公告)号：US08165369B2
公开(公告)日：2012-04-24
申请号：US12243387
申请日：2008-10-01
申请人： Toshiro Kubota , Anna Jerebko , Marcos Salganicoff
发明人： Toshiro Kubota , Anna Jerebko , Marcos Salganicoff
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/187 , G06T2207/30064
摘要： A method for differentiating pulmonary nodules in digitized medical images includes identifying an object of interest from a digital image of the lungs, computing a first distance map of each point of the object of interest, determining a seed point from the first distance map, starting from the seed point, growing a first region by adding successive adjacent layers of points until a background point is reached, and partitioning the first region into a nodule region and a non-nodule region.
摘要翻译：用于区分数字化医学图像中的肺结节的方法包括从肺的数字图像识别感兴趣对象，计算感兴趣对象的每个点的第一距离图，从第一距离图确定种子点，从种子点，通过添加连续的相邻层点直到达到背景点来生长第一区域，并将第一区域划分成结节区域和非结节区域。

2. 发明申请

US20090092302A1 System and Method for Robust Segmentation of Pulmonary Nodules of Various Densities 有权
标题翻译：用于各种密度的肺结节的鲁棒分割的系统和方法
公开(公告)号：US20090092302A1
公开(公告)日：2009-04-09
申请号：US12243387
申请日：2008-10-01
申请人： Toshiro Kubota , Anna Jerebko , Marcos Salganicoff
发明人： Toshiro Kubota , Anna Jerebko , Marcos Salganicoff
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/187 , G06T2207/30064
摘要： A method for differentiating pulmonary nodules in digitized medical images includes identifying an object of interest from a digital image of the lungs, computing a first distance map of each point of the object of interest, determining a seed point from the first distance map, starting from the seed point, growing a first region by adding successive adjacent layers of points until a background point is reached, and partitioning the first region into a nodule region and a non-nodule region.
摘要翻译：用于区分数字化医学图像中的肺结节的方法包括从肺的数字图像识别感兴趣对象，计算感兴趣对象的每个点的第一距离图，从第一距离图确定种子点，从种子点，通过添加连续的相邻层点直到达到背景点来生长第一区域，并将第一区域划分成结节区域和非结节区域。

3. 发明授权

US07876943B2 System and method for lesion detection using locally adjustable priors 有权
标题翻译：使用局部可调节先验的病变检测系统和方法
公开(公告)号：US07876943B2
公开(公告)日：2011-01-25
申请号：US12241183
申请日：2008-09-30
申请人： Anna Jerebko , Marcos Salganicoff , Maneesh Dewan , Harald Steck
发明人： Anna Jerebko , Marcos Salganicoff , Maneesh Dewan , Harald Steck
IPC分类号： G06K9/00 , A61B5/00
CPC分类号： G06K9/6278 , G06K2209/05 , G06K2209/053 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/30028 , G06T2207/30061
摘要： According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a naïve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the naïve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.
摘要翻译：根据本发明的一个方面，一种训练分类器的方法，用于对数字医学图像的计算机辅助诊断中的候选区域进行分类，包括提供注释图像的训练集，每个图像包括已被识别为可疑的一个或多个候选区域，导出一组描述性特征向量，其中每个候选区域与特征向量相关联。特征的子集有条件依赖，其余的特征是有条件的独立的。条件独立的特征用于训练将候选区域分类为病变或非损伤的朴素贝叶斯分类器。从训练图像确定与条件相关特征建模的联合概率分布以及与病变相关联的候选区域的先验概率概率。从初始贝叶斯分类器，联合概率分布和先验概率概率比构成新的分类器。

4. 发明申请

US20090092300A1 System and Method for Lesion Detection Using Locally Adjustable Priors 有权
标题翻译：使用局部可调节的病变检测系统和方法
公开(公告)号：US20090092300A1
公开(公告)日：2009-04-09
申请号：US12241183
申请日：2008-09-30
申请人： Anna Jerebko , Marcos Salganicoff , Maneesh Dewan , Harald Steck
发明人： Anna Jerebko , Marcos Salganicoff , Maneesh Dewan , Harald Steck
IPC分类号： G06K9/00
CPC分类号： G06K9/6278 , G06K2209/05 , G06K2209/053 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/30028 , G06T2207/30061
摘要： According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a naïve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the naïve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.
摘要翻译：根据本发明的一个方面，一种训练分类器的方法，用于对数字医学图像的计算机辅助诊断中的候选区域进行分类，包括提供注释图像的训练集，每个图像包括已被识别为可疑的一个或多个候选区域，导出一组描述性特征向量，其中每个候选区域与特征向量相关联。特征的子集有条件依赖，其余的特征是有条件的独立的。条件独立的特征用于训练将候选区域分类为病变或非损伤的朴素贝叶斯分类器。从训练图像确定与条件相关特征建模的联合概率分布以及与病变相关联的候选区域的先验概率概率。从初始贝叶斯分类器，联合概率分布和先验概率概率比构成新的分类器。

5. 发明申请

US20070110292A1 System and method for computer aided detection via asymmetric cascade of sparse linear classifiers 有权
标题翻译：通过稀疏线性分类器的不对称级联进行计算机辅助检测的系统和方法
公开(公告)号：US20070110292A1
公开(公告)日：2007-05-17
申请号：US11592869
申请日：2006-11-03
申请人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Rao
发明人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Rao
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K9/6282
摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.
摘要翻译：一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选和特征，并且使用形式符号（w）的线性分类器的分级级联将所述异常候选分类为真阳性或假阳性 x + b），其中x是特征向量，w是加权向量，b是模型参数，其中不同的权重用于惩罚假否定和假肯定，并且其中更复杂的特征用于分级器级联的每个连续阶段。

6. 发明授权

US07756313B2 System and method for computer aided detection via asymmetric cascade of sparse linear classifiers 有权
标题翻译：通过稀疏线性分类器的不对称级联进行计算机辅助检测的系统和方法
公开(公告)号：US07756313B2
公开(公告)日：2010-07-13
申请号：US11592869
申请日：2006-11-03
申请人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Bharat Rao
发明人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Bharat Rao
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K9/6282
摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.
摘要翻译：一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选和特征，并且使用形式符号的线性分类器（wTx）的分级级联将所述异常候选分类为真正的肯定或假阳性 + b），其中x是特征向量，w是加权向量，b是模型参数，其中使用不同权重来惩罚假否定和假阳性，并且其中对于级联的每个连续阶段使用更复杂的特征的分类器。

7. 发明授权

US08724866B2 Multi-level contextual learning of data 有权
标题翻译：数据的多层次上下文学习
公开(公告)号：US08724866B2
公开(公告)日：2014-05-13
申请号：US12962901
申请日：2010-12-08
申请人： Dijia Wu , Le Lu , Jinbo Bi , Yoshihisa Shinagawa , Marcos Salganicoff
发明人： Dijia Wu , Le Lu , Jinbo Bi , Yoshihisa Shinagawa , Marcos Salganicoff
IPC分类号： G06K9/00
CPC分类号： G06K9/4638 , G06K2209/05
摘要： Described herein is a framework for automatically classifying a structure in digital image data are described herein. In one implementation, a first set of features is extracted from digital image data, and used to learn a discriminative model. The discriminative model may be associated with at least one conditional probability of a class label given an image data observation Based on the conditional probability, at least one likelihood measure of the structure co-occurring with another structure in the same sub-volume of the digital image data is determined. A second set of features may then be extracted from the likelihood measure.
摘要翻译：这里描述了用于自动分类数字图像数据中的结构的框架。在一个实现中，从数字图像数据中提取第一组特征，并用于学习辨别模型。鉴别模型可以与给定图像数据观察的类标签的至少一个条件概率相关联。基于条件概率，与数字的相同子体积中的另一结构共同出现的结构的至少一个似然度量确定图像数据。然后可以从似然度量中提取第二组特征。

8. 发明申请

US20110075920A1 Multi-Level Contextual Learning of Data 有权
标题翻译：数据的多层次上下文学习
公开(公告)号：US20110075920A1
公开(公告)日：2011-03-31
申请号：US12962901
申请日：2010-12-08
申请人： Dijia Wu , Le Lu , Jinbo Bi , Yoshihisa Shinagawa , Marcos Salganicoff
发明人： Dijia Wu , Le Lu , Jinbo Bi , Yoshihisa Shinagawa , Marcos Salganicoff
IPC分类号： G06K9/62
CPC分类号： G06K9/4638 , G06K2209/05
摘要： Described herein is a framework for automatically classifying a structure in digital image data are described herein. In one implementation, a first set of features is extracted from digital image data, and used to learn a discriminative model. The discriminative model may be associated with at least one conditional probability of a class label given an image data observation Based on the conditional probability, at least one likelihood measure of the structure co-occurring with another structure in the same sub-volume of the digital image data is determined. A second set of features may then be extracted from the likelihood measure.
摘要翻译：这里描述了用于自动分类数字图像数据中的结构的框架。在一个实现中，从数字图像数据中提取第一组特征，并用于学习辨别模型。鉴别模型可以与给定图像数据观察的类标签的至少一个条件概率相关联。基于条件概率，与数字的相同子体积中的另一结构共同出现的结构的至少一个似然度量确定图像数据。然后可以从似然度量中提取第二组特征。

9. 发明授权

US6069967A Method and apparatus for illuminating and imaging eyes through eyeglasses 失效
标题翻译：用于通过眼镜照射和成像眼睛的方法和装置
公开(公告)号：US6069967A
公开(公告)日：2000-05-30
申请号：US964359
申请日：1997-11-04
申请人： J. Michael Rozmus , Marcos Salganicoff
发明人： J. Michael Rozmus , Marcos Salganicoff
IPC分类号： A61B3/14 , G06K9/20 , G06K9/00
CPC分类号： A61B3/14 , G06K9/2018
摘要： A reliable method of illuminating and imaging an eye through eyeglasses uses a monochromatic light source with the smallest possible source area, a camera with an imager that exhibits minimal blooming, and a narrow-bandwidth optical bandpass filter to filter out most of the ambient illumination while passing most of the light from the system's own illuminator. In an alternative embodiment, a partially-transparent mirror is used to make the light source appear to be on the optical axis of the camera as viewed from the subject's eye.
摘要翻译：通过眼镜照射和成像眼睛的可靠方法使用具有尽可能最小的源区域的单色光源，具有最小亮度的成像器的相机和用于滤出大部分环境照明的窄带宽光带通滤波器，通过系统自己的照明器的大部分光线。在替代实施例中，使用部分透明的反射镜来使得从对象眼睛观察到的光源看起来在照相机的光轴上。

10. 发明授权

US5953440A Method of measuring the focus of close-up images of eyes 失效
标题翻译：测量眼睛特写图像焦点的方法
公开(公告)号：US5953440A
公开(公告)日：1999-09-14
申请号：US982364
申请日：1997-12-02
申请人： Guang Hua Zhang , Marcos Salganicoff
发明人： Guang Hua Zhang , Marcos Salganicoff
IPC分类号： A61B5/117 , G06K9/00 , G06K9/48 , G06K9/40
CPC分类号： A61B5/117
摘要： In a method of determining whether an image of an eye is in focus a set of pixels is selected along a line passing through the pupil/iris boundary such that the set contains at least 5 iris portion pixels and at least 5 pupil portion pixels. Statistical values, preferably median values, are computed for all iris pixels in the selected set and for all pupil pixels in the selected set. The step size between the iris pixels and the pupil pixels is computed and absolute gradient values are computed for each pixel. The pixel having a largest absolute gradient value is excluded and an average of the absolute gradient values of the remaining pixels is found. If that average divided by the step size is greater than 0.5 the image is in focus and can be used for identifying a subject whose eye is in the image using iris identification techniques.
摘要翻译：在确定眼睛的图像是否聚焦的方法中，沿着通过瞳孔/虹膜边界的线选择一组像素，使得该组包含至少5个虹膜部分像素和至少5个瞳孔部分像素。对所选集合中的所有虹膜像素和所选集合中的所有瞳孔像素计算统计值，优选中值。计算虹膜像素和瞳孔像素之间的步长，并为每个像素计算绝对梯度值。排除具有最大绝对梯度值的像素，并且找到剩余像素的绝对梯度值的平均值。如果该平均值除以步长大于0.5，则该图像被聚焦，并且可以用于使用虹膜识别技术识别眼睛在图像中的被摄体。

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