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1. 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。

2. 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。

3. 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。

4. 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。

5. 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。

6. 发明授权

US5754938A Pseudonymous server for system for customized electronic identification of desirable objects 失效
标题翻译：用于定制电子识别所需物体的系统的伪服务器
公开(公告)号：US5754938A
公开(公告)日：1998-05-19
申请号：US550886
申请日：1995-10-31
申请人： Frederick S. M. Herz , Jason M. Eisner , Marcos Salganicoff
发明人： Frederick S. M. Herz , Jason M. Eisner , Marcos Salganicoff
IPC分类号： G06F17/30 , G06Q20/38 , G06Q30/02 , H04N5/445 , H04N7/16 , H04N7/167 , H04N7/173 , H04N21/222 , H04N21/231 , H04N21/25 , H04N21/258 , H04N21/262 , H04N21/266 , H04N21/2668 , H04N21/433 , H04N21/442 , H04N21/45 , H04N21/454 , H04N21/462 , H04N21/466 , H04N21/475 , H04N21/4782 , H04N21/81 , H01H1/00 , H01J13/00 , H04N7/14
CPC分类号： H04N21/4665 , G06Q20/383 , G06Q30/02 , H04H60/06 , H04H60/46 , H04L9/0662 , H04L9/0825 , H04N21/222 , H04N21/2223 , H04N21/23106 , H04N21/252 , H04N21/25891 , H04N21/262 , H04N21/26241 , H04N21/26603 , H04N21/2668 , H04N21/4332 , H04N21/44222 , H04N21/4532 , H04N21/454 , H04N21/4622 , H04N21/466 , H04N21/4755 , H04N21/4782 , H04N21/812 , H04N5/44543 , H04N7/163 , H04N7/165 , H04N7/1675 , H04N7/173 , H04N7/17318 , H04N7/17354 , H04H60/31 , H04L2209/60 , Y10S707/99936 , Y10S707/99939
摘要： This invention relates to customized electronic identification of desirable objects, such as news articles, in an electronic media environment, and in particular to a system that automatically constructs both a "target profile" for each target object in the electronic media based, for example, on the frequency with which each word appears in an article relative to its overall frequency of use in all articles, as well as a "target profile interest summary" for each user, which target profile interest summary describes the user's interest level in various types of target objects. The system then evaluates the target profiles against the users' target profile interest summaries to generate a user-customized rank ordered listing of target objects most likely to be of interest to each user so that the user can select from among these potentially relevant target objects, which were automatically selected by this system from the plethora of target objects that are profiled on the electronic media. Users' target profile interest summaries can be used to efficiently organize the distribution of information in a large scale system consisting of many users interconnected by means of a communication network. Additionally, a cryptographically-based pseudonym proxy server is provided to ensure the privacy of a user's target profile interest summary, by giving the user control over the ability of third parties to access this summary and to identify or contact the user.
摘要翻译：本发明涉及电子媒体环境中所需物体（例如新闻物品）的定制电子识别，特别涉及一种系统，其基于例如电子媒体中的每个目标对象自动构建“目标简档” 关于每个词在文章中出现的频率，相对于其在所有文章中的总体使用频率，以及针对每个用户的“目标简档兴趣摘要”，该目标简档兴趣摘要描述了用户的各种类型的兴趣水平目标对象。系统然后根据用户的目标简档兴趣摘要评估目标简档，以生成最有可能对每个用户感兴趣的目标对象的用户定制的排序顺序列表，使得用户可以从这些可能相关的目标对象中进行选择，这是由该系统从在电子媒体上分析的大量目标对象自动选择的。用户的目标配置文件利益摘要可用于有效地组织在由通过通信网络互连的许多用户组成的大型系统中的信息分发。另外，提供了一种基于密码学的假名代理服务器，以通过使用户控制第三方访问此摘要并识别或联系用户的能力来控制用户的目标简档兴趣摘要的隐私。

7. 发明申请

US20140294263A1 Synchronized Navigation of Medical Images 有权
标题翻译：医学图像同步导航
公开(公告)号：US20140294263A1
公开(公告)日：2014-10-02
申请号：US13853174
申请日：2013-03-29
申请人： Gerardo Hermosillo Valadez , Marcos Salganicoff , Matthias Wolf , Xiang Sean Zhou , Yiqiang Zhan
发明人： Gerardo Hermosillo Valadez , Marcos Salganicoff , Matthias Wolf , Xiang Sean Zhou , Yiqiang Zhan
IPC分类号： G06T7/00
CPC分类号： G06T7/0028 , G06T7/33 , G06T2207/30004
摘要： Disclosed herein is a framework for facilitating synchronized image navigation. In accordance with one aspect, at least first and second medical images are received. A non-linear mapping between the first and second medical images is generated. A selection of a given location in the first medical image is received in response to a user's navigational operation. Without deforming the second medical image, a target location in the second medical image is determined by using the non-linear mapping. The target location corresponds to the given location in the first medical image. An optimized deformation-free view of the second medical image is generated based at least in part on the target location. While the user performs navigational operations on the first medical image, the framework repeatedly receives the selection of the given location, determines the target location using the non-linear mapping, and generates the optimized deformation-free view of the second medical image based at least in part on the target location.
摘要翻译：这里公开了一种促进同步图像导航的框架。根据一个方面，至少接收第一和第二医学图像。产生第一和第二医学图像之间的非线性映射。响应于用户的导航操作接收对第一医疗图像中的给定位置的选择。在不使第二医用图像变形的情况下，通过使用非线性映射来确定第二医用图像中的目标位置。目标位置对应于第一医疗图像中的给定位置。至少部分地基于目标位置产生第二医疗图像的优化的无变形视图。当用户在第一医学图像上执行导航操作时，框架重复地接收对给定位置的选择，使用非线性映射确定目标位置，并且至少基于第二医学图像生成优化的无变形视图部分在目标位置。

8. 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。

9. 发明授权

US07961923B2 Method for detection and visional enhancement of blood vessels and pulmonary emboli 有权
标题翻译：血管和肺栓塞的检测和视力增强的方法
公开(公告)号：US07961923B2
公开(公告)日：2011-06-14
申请号：US11841168
申请日：2007-08-20
申请人： Senthil Periaswamy , Marcos Salganicoff
发明人： Senthil Periaswamy , Marcos Salganicoff
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06K9/4609 , G06K2209/05 , G06T5/50 , G06T7/12 , G06T2207/10072 , G06T2207/10116 , G06T2207/30061 , G06T2207/30101
摘要： A method for detecting a substantially cylindrical internal structures and dark structures surrounded by bright intensity values (contrast) in a medical image includes acquiring a medical image. A gradient of the medical image is calculated. Local shape index information for the calculated gradient of the medical image is calculated. Gradient information having a local shape index not indicative of a ridge and rut shapes is removed. Diverging gradient field responses (DGFR) are calculated based on the remaining gradient information. The DGFR responses and relative amount of DGFR responses for the rut and ridge areas is used as a discriminative feature in detecting the substantially cylindrical internal structure as well as darker occluding structures within cylindrical structures such as Pulmonary Emboli.
摘要翻译：用于检测医疗图像中由亮度值（对比度）包围的基本上圆柱形的内部结构和暗结构的方法包括获取医学图像。计算医学图像的梯度。计算医学图像的计算梯度的局部形状指数信息。具有不表示脊和车辙形状的局部形状指标的梯度信息被去除。基于剩余梯度信息计算发散梯度场响应（DGFR）。用于车辙和脊部区域的DGFR响应和DGFR响应的相对量用作检测圆柱形结构（例如肺栓塞）中的基本上圆柱形的内部结构以及较暗的闭塞结构的鉴别特征。

10. 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。

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