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1. 发明授权

US06901156B2 Method, system and computer readable medium for an intelligent search workstation for computer assisted interpretation of medical images 有权
标题翻译：智能搜索工作站的方法，系统和计算机可读介质，用于医学图像的计算机辅助解释
公开(公告)号：US06901156B2
公开(公告)日：2005-05-31
申请号：US09773636
申请日：2001-02-02
申请人： Maryellen L. Giger , Carl J. Vyborny , Zhimin Huo , Li Lan
发明人： Maryellen L. Giger , Carl J. Vyborny , Zhimin Huo , Li Lan
IPC分类号： A61B6/00 , G06F19/00 , G06K9/00 , G06Q50/00 , G06T1/00 , G06T7/00
CPC分类号： G06F19/321 , A61B6/502 , G06F19/00 , G06T7/0012 , G06T2207/30068 , G16H50/20 , G16H50/70
摘要： A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists. Techniques include novel developments and implementations of computer-extracted features for similarity calculation and novel methods for the display of the unknown case amongst known cases with and without the computer-determined diagnoses.
摘要翻译：一种用于智能搜索显示器的方法，系统和计算机可读介质，已经并入了自动计算机图像分析。在观察未知的乳房X线照相术的情况下，显示器显示计算机分类输出以及具有已知诊断（例如恶性与良性）的病变的图像以及类似的计算机提取的特征。搜索中使用的相似性索引可以由放射科医师选择，以基于单个特征，多个特征或计算机估计恶性肿瘤的可能性。具体地说，系统包括计算已知数据库中的图像的特征，计算未知情况的特征，计算相似性指数，沿着未知情况存在的概率分布曲线显示已知情况。技术包括用于相似性计算的计算机提取特征的新颖开发和实现，以及用于在具有和不具有计算机确定的诊断的情况下在已知病例中显示未知病例的新颖方法。

2. 发明授权

US07184582B2 Method, system and computer readable medium for an intelligent search workstation for computer assisted interpretation of medical images 有权
标题翻译：智能搜索工作站的方法，系统和计算机可读介质，用于医学图像的计算机辅助解释
公开(公告)号：US07184582B2
公开(公告)日：2007-02-27
申请号：US10793799
申请日：2004-03-08
申请人： Maryellen L. Giger , Carl J. Vyborny , Zhimin Huo , Li Lan
发明人： Maryellen L. Giger , Carl J. Vyborny , Zhimin Huo , Li Lan
IPC分类号： G06K9/00
CPC分类号： G06F19/321 , A61B6/502 , G06F19/00 , G06T7/0012 , G06T2207/30068 , G16H50/20 , G16H50/70
摘要： A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists. Techniques include novel developments and implementations of computer-extracted features for similarity calculation and novel methods for the display of the unknown case amongst known cases with and without the computer-determined diagnoses.
摘要翻译：一种用于智能搜索显示器的方法，系统和计算机可读介质，已经并入了自动计算机图像分析。在观察未知的乳房X线照相术的情况下，显示器显示计算机分类输出以及具有已知诊断（例如恶性与良性）的病变的图像以及类似的计算机提取的特征。搜索中使用的相似性索引可以由放射科医师选择，以基于单个特征，多个特征或计算机估计恶性肿瘤的可能性。具体地说，系统包括计算已知数据库中的图像的特征，计算未知情况的特征，计算相似性指数，沿着未知情况存在的概率分布曲线显示已知情况。技术包括用于相似性计算的计算机提取特征的新颖开发和实现，以及用于在具有和不具有计算机确定的诊断的情况下在已知病例中显示未知病例的新颖方法。

3. 发明授权

US07123762B2 Method and system for risk-modulated diagnosis of disease 有权
公开(公告)号：US07123762B2
公开(公告)日：2006-10-17
申请号：US10360814
申请日：2003-02-10
申请人： Maryellen L. Giger , Zhimin Huo , Carl J. Vyborny
发明人： Maryellen L. Giger , Zhimin Huo , Carl J. Vyborny
IPC分类号： G06K9/00 , A61B5/00
CPC分类号： G06T7/0012 , G06K9/3233 , G06T2207/30068 , G16H50/20 , G16H50/30 , G16H50/50 , G16H50/70
摘要： A method of calculating a disease assessment by analyzing a medical image, comprising (1) extracting at least one lesion feature value from the medical image; (2) extracting at least one risk feature value from the medical image; and (3) determining the disease assessment based on the at least one lesion feature value and the at least one risk feature value. The method employs lesion characterization for characterizing the lesion, and risk assessment based on the lesion's surroundings, i.e., the environment local and distal to the lesion. Computerized methods both characterize mammographic lesions and assess the breast parenchymal pattern on mammograms, resulting in improved characterization of lesions for specific subpopulations, combining the benefits of both techniques.

4. 发明授权

US07769215B2 Method, system, and medium for prevalence-based computerized analysis of medical images and information 有权
标题翻译：方法，系统和媒介，用于基于流行率的医学图像和信息的计算机化分析
公开(公告)号：US07769215B2
公开(公告)日：2010-08-03
申请号：US10997935
申请日：2004-11-29
申请人： Karla Horsch , Maryellen L. Giger , Charles E. Metz , Carl J. Vyborny , Terrieann Vyborny, legal representative , Li Lan
发明人： Karla Horsch , Maryellen L. Giger , Charles E. Metz , Carl J. Vyborny , Li Lan
IPC分类号： G06K9/00
CPC分类号： G06F19/321 , G06F19/00
摘要： A method for computer-assisted interpretation of medical images that factor in characteristics of an individual performing the interpretation. The method automatically determines and/or incorporates prevalence-based computer analysis based on an estimated likelihood of a pathological state, e.g., a malignancy. A system implementing the method includes the calculation of features or other characteristics of images in a known database, calculation of features of an unknown case, calculation of the probability (or likelihood) of disease state, calculation of the modified computer output that includes the internal prevalence (or internal decision-making process) of the user (or group of users), and output of the result.
摘要翻译：一种用于计算机辅助解释医疗图像的方法，其用于考虑执行解释的个体的特征。该方法基于病理状态（例如恶性肿瘤）的估计可能性来自动确定和/或结合基于流行率的计算机分析。实现该方法的系统包括计算已知数据库中的图像的特征或其他特征，计算未知病例的特征，计算疾病状态的概率（或可能性），计算包括内部的修改的计算机输出用户（或用户组）的流行（或内部决策过程）以及结果的输出。

5. 发明授权

US07418123B2 Automated method and system for computerized image analysis for prognosis 有权
标题翻译：计算机图像分析自动化方法和系统预后
公开(公告)号：US07418123B2
公开(公告)日：2008-08-26
申请号：US10617675
申请日：2003-07-14
申请人： Maryellen L. Giger , Ioana Bonta , Ruth Heimann , Robert M. Nishikawa , Carl J. Vyborny
发明人： Maryellen L. Giger , Ioana Bonta , Ruth Heimann , Robert M. Nishikawa , Carl J. Vyborny
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/187 , G06T2207/10088 , G06T2207/10132 , G06T2207/30068 , G06T2207/30096
摘要： An automated method for determining prognosis based on an analysis of abnormality (lesion) features and parenchymal features obtained from medical image data of a patient. The techniques include segmentation of lesions from radiographic images, extraction of lesion features, and a merging of the features (with and without clinical information) to yield as estimate of the prognosis for the specific case. An example is given for the prognosis of breast cancer lesions using mammographic data. A computerized image analysis system for assessing prognosis combines the computerized analysis of medical images of cancerous lesions with the training-based methods of assessing prognosis of a patient, using indicators such as lymph node involvement, presence of metastatic disease, local recurrence, and/or death. It is expected that use of such a system to assess the severity of the disease will aid in improved decision-making regarding treatment options.
摘要翻译：一种用于基于从患者的医学图像数据获得的异常（损伤）特征和实质特征的分析来确定预后的自动化方法。这些技术包括从放射照相图像分割病变，提取病变特征，以及特征（有和没有临床信息）的合并，以产生具体病例的预后估计。使用乳房X线摄影数据给出乳腺癌病变预后的一个例子。用于评估预后的计算机化图像分析系统将癌症病变的医学图像的计算机化分析与使用诸如淋巴结受累，转移性疾病的存在，局部复发的指标以及/或患者的评估患者预后的基于训练的方法相结合死亡。预计使用这样的系统来评估疾病的严重程度将有助于改善治疗方案的决策。

6. 发明授权

US06855114B2 Automated method and system for the detection of abnormalities in sonographic images 有权
标题翻译：超声图像检测异常的自动化方法和系统
公开(公告)号：US06855114B2
公开(公告)日：2005-02-15
申请号：US10126523
申请日：2002-04-22
申请人： Karen Drukker , Maryellen L. Giger , Karla Horsch , Carl J. Vyborny
发明人： Karen Drukker , Maryellen L. Giger , Karla Horsch , Carl J. Vyborny
IPC分类号： A61B8/08 , G06T7/00 , A61B8/00
CPC分类号： G06T7/0012 , A61B8/0825
摘要： A method of detecting a candidate abnormality in a sonographic medical image, based on determining a radial gradient index (RGI) at plural pixels, producing an RGI image, thresholding the RGI image, determining a candidate abnormality based on the thresholding step, and locating a center point of the candidate abnormality. The candidate abnormality may be classified by segmenting the candidate abnormality, including determining average radial gradients (ARDs) in the sonographic medical image based on the center point, extracting plural features from the segmented candidate abnormality, and determining a likelihood of the candidate abnormality being an actual abnormality based on the extracted plural features.
摘要翻译：基于确定多个像素处的径向梯度指数（RGI），产生RGI图像，阈值化RGI图像，基于阈值步骤确定候选异常，以及定位一个检测超声医学图像中候选异常的方法候选异常的中心点。候选异常可以通过分割候选异常进行分类，包括基于中心点确定超声医学图像中的平均径向梯度（ARD），从分割的候选异常中提取多个特征，以及确定候选异常的可能性为基于提取的多个特征的实际异常。

7. 发明授权

US5832103A Automated method and system for improved computerized detection and classification of massess in mammograms 失效
标题翻译：自动化方法和系统，用于改进乳房X线照片中的Massess检测和分类
公开(公告)号：US5832103A
公开(公告)日：1998-11-03
申请号：US515798
申请日：1995-08-16
申请人： Maryellen L. Giger , Kunio Doi , Ping Lu , Zhimin Huo
发明人： Maryellen L. Giger , Kunio Doi , Ping Lu , Zhimin Huo
IPC分类号： A61B6/00 , G06K9/00 , G06T1/00 , G06T7/00
CPC分类号： G06K9/00127 , G06T7/0012
摘要： A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.
摘要翻译：乳房X线照片自动检测和分类质量的方法和系统。这些方法和系统包括迭代，多级灰度阈值的表现，其次是病灶提取和特征提取技术，用于从假阳性肿块和良性肿块中分离真实肿块。该方法和系统提供了对质量检测的改进，包括处理图像的多灰度阈值处理，以增加灵敏度和准确的区域生长和特征分析以增加特异性。质量分类的新改进包括相对于所讨论的像素的径向角的累积边缘梯度取向直方图分析; 即在质量的边缘周围或在所讨论的质量块内或周围。质量的分类导致恶性肿瘤的可能性。

8. 发明授权

US06282305B1 Method and system for the computerized assessment of breast cancer risk 失效
标题翻译：乳腺癌风险计算机化评估方法与系统
公开(公告)号：US06282305B1
公开(公告)日：2001-08-28
申请号：US09092004
申请日：1998-06-05
申请人： Zhimin Huo , Maryellen L. Giger
发明人： Zhimin Huo , Maryellen L. Giger
IPC分类号： G06K900
CPC分类号： G06T7/0012 , G06K9/6277
摘要： A method, system and computer readable medium for the computerized assessment of breast cancer risk, wherein a digital image of a breast is obtained and at least one feature, and typically plural features, are extracted from a region of interest in the digital. The extracted features are compared with a predetermined model associating patterns of the extracted features with a risk estimate derived from corresponding feature patterns associated with a predetermined model based on gene carrier information or clinical information, or both gene carrier information and clinical information, and a risk classification index is output as a result of the comparison. Preferred features to be extracted from the digital image include 1) one or more features based on absolute values of gray levels of pixels in said region of interest, 2) one or more features based on gray-level histogram analysis of pixels in said region of interest; 3) one or more features based on Fourier analysis of pixel values in said region of interest; and 4) one or more features based on a spatial relationship among gray levels of pixels within the region of interest.
摘要翻译：一种用于乳腺癌风险的计算机化评估的方法，系统和计算机可读介质，其中获得乳房的数字图像，并且从数字的感兴趣区域中提取至少一个特征，并且通常为多个特征。将提取的特征与预定模型进行比较，所述预定模型将提取的特征的模式与基于基因载体信息或临床信息或基因载体信息和临床信息两者相关联的相应特征模式相关联的风险估计相关联，以及风险作为比较的结果输出分类索引。要从数字图像提取的优选特征包括1）基于所述感兴趣区域中的像素的灰度级的绝对值的一个或多个特征，2）基于所述区域中的像素的灰度级直方图分析的一个或多个特征利益; 3）基于所述感兴趣区域中的像素值的傅里叶分析的一个或多个特征; 以及4）基于感兴趣区域内的像素的灰度级之间的空间关系的一个或多个特征。

9. 发明授权

US06898303B2 Method, system and computer readable medium for the two-dimensional and three-dimensional detection of lesions in computed tomography scans 失效
标题翻译：用于二维和三维检测计算机断层扫描中病变的方法，系统和计算机可读介质
公开(公告)号：US06898303B2
公开(公告)日：2005-05-24
申请号：US09759333
申请日：2001-01-16
申请人： Samuel G. Armato, III , Maryellen L. Giger , Heber Macmahon
发明人： Samuel G. Armato, III , Maryellen L. Giger , Heber Macmahon
IPC分类号： A61B6/03 , G06T1/00 , G06T5/00 , G06T7/00 , G06T7/60 , G06T17/40 , G06K9/00
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/174 , G06T7/187 , G06T2207/10081 , G06T2207/10124 , G06T2207/20061 , G06T2207/30064
摘要： A method, system and computer readable medium for automated detection of lung nodules in computed tomography (CT) image scans, including generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from the CT image scans; generating three-dimensional segmented lung volume images by combining the two-dimensional segmented lung images; determining three-dimensional lung nodule candidates from the three-dimensional segmented lung volume images, including, identifying structures within the three-dimensional segmented lung volume images that meet a volume criterion; deriving features from the lung nodule candidates; and detecting lung nodules by analyzing the features to eliminate false-positive nodule candidates from the nodule candidates.
摘要翻译：一种用于在计算机断层摄影（CT）图像扫描中自动检测肺结节的方法，系统和计算机可读介质，包括通过分割从CT图像扫描导出的多个二维CT图像部分来生成二维分割的肺图像; 通过组合二维分割肺图像产生三维分段肺容积图像; 从三维分段的肺容积图像中确定三维肺结节候选物，包括识别符合体积标准的三维分段肺容量图像内的结构; 从肺结节候选者得出特征; 并通过分析特征以消除结节候选物中的假阳性结节候选物来检测肺结节。

10. 发明授权

US6138045A Method and system for the segmentation and classification of lesions 有权
公开(公告)号：US6138045A
公开(公告)日：2000-10-24
申请号：US131162
申请日：1998-08-07
申请人： Matthew A. Kupinski , Maryellen L. Giger
发明人： Matthew A. Kupinski , Maryellen L. Giger
IPC分类号： G01R33/32 , A61B5/00 , A61B5/055 , A61B6/00 , A61B6/03 , A61B8/00 , G06T1/00 , G06T5/00 , G06T7/00 , A61B5/05
CPC分类号： G06T7/0012 , G06T7/0081 , G06T7/0083 , G06T7/0087 , G06T2207/10116 , G06T2207/20156 , G06T2207/30068 , G06T2207/30101
摘要： A method for the automated segmentation of an abnormality in a medical image, including acquiring first image data representative of the medical image; locating a suspicious site at which the abnormality may exist; establishing a seed point within the suspicious site; and preprocessing the suspicious site with a constraint function to produce second image data in which pixel values distant of the seed point are suppressed. Preprocessing includes using an isotropic Gaussian function centered on the seed point as the constraint function, or for example using an isotropic three dimensional Gaussian function centered on the seed point as the constraint function. The method further includes applying plural thresholds to the second image data to partition the second image data at each threshold; identifying corresponding first image data for the partitioned second image data obtained at each respective threshold; determining a respective index for each of the partitioned first image data; and determining a preferred partitioning, for example that partitioning leading to a maximum index value, based on the indices determined at each threshold, and segmenting the lesion based on the partitioning established by the threshold resulting in the maximum index. If desired, the first image data with the partitioning defined by the threshold which is determined to result in the maximum index, is then displayed. A system and computer readable storage medium are also provided, likewise using the radial gradient index (RGI) or a simple probabilistic models to segment mass lesions, or other similar nodular structures, from surrounding background. In the system, a series of image partitions is likewise created using gray-level information as well as prior knowledge of the shape of typical mass lesions. When the RGI is used, the partition that maximizes RGI is selected. When a probability model is used, probability distributions for gray-levels inside and outside the partitions are estimated, and subsequently used to determine the probability that the image occurred for each given partition. The partition that maximizes this probability is selected as the final lesion partition (contour).

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