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1. 发明申请

US20160078624A1 METHOD, SYSTEM, SOFTWARE AND MEDIUM FOR ADVANCED INTELLIGENT IMAGE ANALYSIS AND DISPLAY OF MEDICAL IMAGES AND INFORMATION 审中-公开
标题翻译：方法，系统，软件和媒介用于高级智能图像分析和显示医学图像和信息
公开(公告)号：US20160078624A1
公开(公告)日：2016-03-17
申请号：US14887678
申请日：2015-10-20
申请人： Maryellen L. GIGER , Robert TOMEK , Jeremy Bancroft BROWN , Andrew Robert JAMIESON , Li LAN , Michael R. CHINANDER , Karen DRUKKER , Hui Li , Neha BHOOSHAN , Gillian NEWSTEAD
发明人： Maryellen L. GIGER , Robert TOMEK , Jeremy Bancroft BROWN , Andrew Robert JAMIESON , Li LAN , Michael R. CHINANDER , Karen DRUKKER , Hui Li , Neha BHOOSHAN , Gillian NEWSTEAD
IPC分类号： G06T7/00 , G06T7/40 , G06T19/20 , G06T15/08 , G06T17/00
CPC分类号： G06T7/0016 , G06K9/6253 , G06T7/0012 , G06T7/40 , G06T7/62 , G06T11/003 , G06T15/08 , G06T15/20 , G06T17/00 , G06T19/20 , G06T2207/10076 , G06T2207/10084 , G06T2207/10088 , G06T2207/10116 , G06T2207/10136 , G06T2207/20081 , G06T2207/30004 , G06T2207/30068
摘要： Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyses. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique. Output to 3D shows relationships of the unknown case to a cloud of known or unlabeled cases, in which the cloud demonstrates the structure of the population of patients with and without the disease.
摘要翻译：医学图像的计算机化解释用于定量分析多形态乳房图像，包括FFDM，2D / 3D超声，MRI或其他乳房成像方法的分析。提供肿瘤和背景组织的实时表征，以及基于图像的生物标志物的计算，用于乳腺癌检测，诊断，预后，风险评估和治疗反应。分析包括病变分割，以及基于病变或体素分析的相关特征（纹理/形态/动力特征）的提取。提供了使用人工智能的几个分类任务中的特征组合。根据1D，2D或3D分布的输出，其中相对于已知或未标记的情况下的计算来识别未知情况，可以通过维度降低技术。输出到3D显示未知病例与已知或未标记病例的云的关系，其中云显示患有和没有疾病的患者群体的结构。

2. 发明授权

US09208556B2 Method, system, software and medium for advanced intelligent image analysis and display of medical images and information 有权
标题翻译：方法，系统，软件和媒介，用于高级智能图像分析和医学图像和信息的显示
公开(公告)号：US09208556B2
公开(公告)日：2015-12-08
申请号：US13305495
申请日：2011-11-28
申请人： Maryellen L. Giger , Robert Tomek , Jeremy Bancroft Brown , Andrew Robert Jamieson , Li Lan , Michael R. Chinander , Karen Drukker , Hui Li , Neha Bhooshan , Gillian Newstead
发明人： Maryellen L. Giger , Robert Tomek , Jeremy Bancroft Brown , Andrew Robert Jamieson , Li Lan , Michael R. Chinander , Karen Drukker , Hui Li , Neha Bhooshan , Gillian Newstead
IPC分类号： G06T7/00 , G06T11/00 , G06T15/08 , G06T15/20 , G06T17/00 , G06T19/20 , G06K9/62 , G06T7/60
CPC分类号： G06T7/0016 , G06K9/6253 , G06T7/0012 , G06T7/40 , G06T7/62 , G06T11/003 , G06T15/08 , G06T15/20 , G06T17/00 , G06T19/20 , G06T2207/10076 , G06T2207/10084 , G06T2207/10088 , G06T2207/10116 , G06T2207/10136 , G06T2207/20081 , G06T2207/30004 , G06T2207/30068
摘要： Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyzes. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique. Output to 3D shows relationships of the unknown case to a cloud of known or unlabeled cases, in which the cloud demonstrates the structure of the population of patients with and without the disease.
摘要翻译：医学图像的计算机化解释用于定量分析多形态乳房图像，包括FFDM，2D / 3D超声，MRI或其他乳房成像方法的分析。提供肿瘤和背景组织的实时表征，以及基于图像的生物标志物的计算，用于乳腺癌检测，诊断，预后，风险评估和治疗反应。分析包括病变分割，以及基于病变或体素分析的相关特征（纹理/形态/动力特征）的提取。提供了使用人工智能的几个分类任务中的特征组合。根据1D，2D或3D分布的输出，其中相对于已知或未标记的情况下的计算来识别未知情况，可以通过维度降低技术。输出到3D显示未知病例与已知或未标记病例的云的关系，其中云显示患有和没有疾病的患者群体的结构。

3. 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。

4. 发明授权

US07298881B2 Method, system, and computer software product for feature-based correlation of lesions from multiple images 有权
标题翻译：方法，系统和计算机软件产品，用于来自多个图像的病变的基于特征的相关性
公开(公告)号：US07298881B2
公开(公告)日：2007-11-20
申请号：US11056368
申请日：2005-02-14
申请人： Maryellen L Giger , HuiHua Wen , Li Lan
发明人： Maryellen L Giger , HuiHua Wen , Li Lan
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06F19/00 , G06T7/33 , G06T7/35 , G06T7/97 , G06T2207/10116 , G06T2207/10132 , G06T2207/30068 , Y10S128/922
摘要： A method, system, and computer software product for correlating medical images, comprising: obtaining first image data representative of a first medical image including a first abnormality; obtaining second image data representative of a second medical image including a second abnormality; determining at least one feature value for each of the first and second abnormalities using the first and second image data; calculating, based on the determined feature values, a likelihood value indicative of a likelihood that the first and second abnormalities are a same abnormality; and outputting the determined likelihood value.
摘要翻译：一种用于使医学图像相关的方法，系统和计算机软件产品，包括：获得表示包括第一异常的第一医学图像的第一图像数据; 获取表示包括第二异常的第二医用图像的第二图像数据; 使用所述第一和第二图像数据确定所述第一和第二异常中的每一个的至少一个特征值; 基于所确定的特征值计算表示所述第一和第二异常是相同异常的可能性的似然值; 并输出所确定的似然值。

5. 发明授权

US06282307B1 Method and system for the automated delineation of lung regions and costophrenic angles in chest radiographs 失效
标题翻译：胸部X光照片自动划分肺部区域和肋间角度的方法和系统
公开(公告)号：US06282307B1
公开(公告)日：2001-08-28
申请号：US09028518
申请日：1998-02-23
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： G06K900
CPC分类号： G06K9/38 , A61B6/50 , G06T7/11 , G06T7/12 , G06T7/66 , G06T2207/10116 , G06T2207/20012 , G06T2207/30061
摘要： A method, system, and computer product for the automated segmentation of the lung fields and costophrenic angle (CP) regions in posteroanterior (PA) chest radiographs wherein image segmentation based on gray-level threshold analysis is performed by applying an iterative global gray-level thresholding method to a chest image based on the features of a global gray-level histogram. Features of the regions in a binary image constructed at each iteration are identified and analyzed to exclude regions external to the lung fields. The initial lung contours that result from this global process are used to facilitate a local gray-level thresholding method. Individual regions-of-interest (ROIs) are placed along the initial contour. A procedure is implemented to determine the gray-level thresholds to be applied to the pixels within the individual ROIs. The result is a binary image, from which final contours are constructed. Smoothing processes are applied, including a unique adaptation of a rolling ball method. CP angles are identified and delineated by using the lung segmentation contours as a means of placing ROIs that capture the CP angle regions. Contrast-based information is employed on a column-by-column basis to identify initial diaphragm points, and maximum gray-level information is used on a row-by-row basis to identify initial costal points. Analysis of initial diaphragm and costal points allows for appropriate adjustment of CP angle ROI positioning. Polynomial curve-fitting is used to combine the diaphragm and costal points into a continuous, smooth CP angle delineation. This delineation is then spliced into the final lung segmentation contours. In addition, quantitative information derived from the CP angle delineations is used to assess the presence of abnormal CP angles.
摘要翻译：一种用于在前后（PA）胸片中自动分割肺区和肋间角（CP）区域的方法，系统和计算机产品，其中基于灰度阈值分析的图像分割通过应用迭代全局灰度级基于全局灰度直方图的特征对胸部图像进行阈值处理。识别和分析在每个迭代构建的二进制图像中的区域的特征以排除肺部外部的区域。由该全局过程产生的初始肺轮廓用于促进局部灰度阈值法。单个感兴趣区域（ROI）沿初始轮廓放置。实施一个程序来确定要应用于各个ROI内的像素的灰度级阈值。结果是二进制图像，从中构建最终轮廓。应用平滑过程，包括滚球法的独特适应性。通过使用肺分割轮廓作为放置捕获CP角度区域的ROI的手段来识别和描绘CP角度。基于对比度的信息逐列采用以识别初始膜片点，并且逐行地使用最大灰度级信息来识别初始折射点。分析初始膜片和肋点可以适当调整CP角度ROI定位。多项式曲线拟合用于将隔膜和肋点组合成连续，平滑的CP角度描绘。然后将该描绘拼接成最终的肺分割轮廓。此外，使用从CP角度描绘导出的定量信息来评估异常CP角度的存在。

6. 发明授权

US06185320B2 Method and system for detection of lesions in medical images 失效
标题翻译：用于检测医学图像病变的方法和系统
公开(公告)号：US06185320B2
公开(公告)日：2001-02-06
申请号：US08982282
申请日：1997-12-01
申请人： Ulrich Bick , Maryellen L. Giger
发明人： Ulrich Bick , Maryellen L. Giger
IPC分类号： G06K900
CPC分类号： G06K9/482 , G06T7/0012 , G06T7/11 , G06T7/12 , G06T7/149 , G06T7/155 , G06T7/187 , G06T2207/20056 , G06T2207/20156 , G06T2207/30068 , G06T2207/30096
摘要： A method and system for the automated detection of lesions in medical images. Medical images, such as mammograms are segmented and optionally processing with peripheral enhancement and/or modified median filtering. A modified morphological open operation and filtering with a modified mass filter are performed for the initial detection of circumscribed lesions. Then, the lesions are matched using a deformable shape template with Fourier descriptors. Characterization of the match is done using simulated annealing, and measuring the circularity and density characteristics of the suspected lesion. The procedure is performed iteratively at different spatial resolution in which at each resolution step a specific lesion size is detected. The detection of the lesion leads to a localization of a suspicious region and thus the likelihood of cancer.
摘要翻译：一种用于自动检测医学图像中病变的方法和系统。医疗图像，例如乳房X线照片被分割，并且可选地使用外围增强和/或修改的中值滤波进行处理。进行修改后的形态开放操作和使用改进的质量过滤器进行过滤以初步检测外切损伤。然后，使用具有傅立叶描述符的可变形形状模板匹配病变。使用模拟退火进行匹配的表征，并测量疑似病变的圆形度和密度特征。以不同的空间分辨率迭代地执行该过程，其中在每个分辨率步骤检测到特定的病变大小。病变的检测导致可疑区域的定位，从而导致癌症的可能性。

7. 发明授权

US5984870A Method and system for the automated analysis of lesions in ultrasound images 失效
标题翻译：超声图像自动分析病变的方法和系统
公开(公告)号：US5984870A
公开(公告)日：1999-11-16
申请号：US900192
申请日：1997-07-25
申请人： Maryellen L. Giger , Hania Al-Hallaq , Dulcy Wolverton , Ulrich Bick
发明人： Maryellen L. Giger , Hania Al-Hallaq , Dulcy Wolverton , Ulrich Bick
IPC分类号： A61B20060101 , A61B8/00 , G06T7/00
CPC分类号： G06T7/0012 , G06T2207/30068
摘要： A method and apparatus for the computerized automatic analysis of lesions in ultrasound images, including the computerized analysis of lesions in the breast, using gradient, gray-level, and texture based measures. Echogenicity features are developed to assess the characteristics of the lesions and in some cases give an estimate of the likelihood of malignancy or of prognosis. The output from the computerized analysis is used in making a diagnosis and/or prognosis. For example, with the analysis of the ultrasound images of the breast, the features can be used to either distinguish between malignant and benign lesions, or distinguish between (i.e., diagnosis) the types of benign lesions such as benign solid lesions (e.g., fibroadenoma), simple cysts, complex cysts, and benign cysts. The ultrasound image features can be merged with those from mammographic and/or magnetic resonance images of the same lesion for classification by means of a common artificial neural network.
摘要翻译：一种用于计算机化自动分析超声图像中病变的方法和装置，包括使用梯度，灰度和基于纹理的手段对乳房病变进行计算机化分析。开发Echogenicity特征来评估病变的特征，并且在某些情况下给出恶性肿瘤或预后的可能性的估计。计算机分析的输出用于诊断和/或预后。例如，通过对乳房超声图像的分析，这些特征可以用于区分恶性和良性病变，或区分（即，诊断）良性病变的类型，例如良性固体病变（例如，纤维腺瘤），简单囊肿，复杂囊肿和良性囊肿。超声图像特征可以与来自同一病变的乳房X线照相和/或磁共振图像的那些融合，以便通过普通人造神经网络分类。

8. 发明授权

US5452367A Automated method and system for the segmentation of medical images 失效
标题翻译：医学图像分割的自动化方法和系统
公开(公告)号：US5452367A
公开(公告)日：1995-09-19
申请号：US158320
申请日：1993-11-29
申请人： Ulrich Bick , Maryellen L. Giger
发明人： Ulrich Bick , Maryellen L. Giger
IPC分类号： A61B6/00 , G06T1/00 , G06T5/00 , G06T5/30 , G06T7/00 , G06F159/00
CPC分类号： G06K9/342 , G06T7/0012 , G06T7/0081 , G06T7/0083 , G06T7/0091 , G06K2209/05 , G06T2207/10116 , G06T2207/20036 , G06T2207/20041 , G06T2207/20141 , G06T2207/20148 , G06T2207/30068
摘要： A method for the automated segmentation of medical images, including generating image data from radiographic images of the breast. The method is applicable to breast mammograms including the extraction of the skinline as well as correction for non-uniform exposure conditions, hand radiographs, and chest radiographs. Techniques for the segmentation include noise filtering, local gray value range determination, modified global histogram analysis, region growing and determination of object contour. The method also is applicable to skin detection and analysis of skin thickening in medical images, where image segmentation, local optimization of external skinline, creation of a gradient image, identification of the internal skinline and then skin thickness determination are carried out. The method for enhancement of medical images includes, after image segmentation and identification of the skinline, calculation of pixel distances from the skinline, determination of the enhancement parameter fit and then selective enhancement of the periphery.
摘要翻译：一种用于医学图像的自动分割的方法，包括从乳房的放射照相图像生成图像数据。该方法适用于乳房X线照片，包括皮肤的提取，以及矫正不均匀的暴露条件，手放射线照片和胸片。用于分割的技术包括噪声滤波，局部灰度值范围确定，修改的全局直方图分析，区域增长和对象轮廓的确定。该方法还适用于医学图像中皮肤增厚的皮肤检测和分析，其中进行图像分割，外部皮肤线的局部优化，梯度图像的创建，内部皮肤线的鉴定，然后进行皮肤厚度测定。用于增强医学图像的方法包括在皮肤图像分割和识别之后，从皮肤线计算像素距离，确定增强参数拟合，然后选择性地增强周边。

9. 发明授权

US07983732B2 Method, system, and computer software product for automated identification of temporal patterns with high initial enhancement in dynamic magnetic resonance breast imaging 有权
标题翻译：方法，系统和计算机软件产品，用于自动识别动态磁共振乳房成像中高初始增强的时间模式
公开(公告)号：US07983732B2
公开(公告)日：2011-07-19
申请号：US11056366
申请日：2005-02-14
申请人： Weijie Chen , Maryellen L Giger , Gillian Newstead
发明人： Weijie Chen , Maryellen L Giger , Gillian Newstead
IPC分类号： A61B5/00
CPC分类号： G06T7/0012 , G06T2207/30068
摘要： A method, system, and computer software product for analyzing medical images, including obtaining image data representative of a plurality of medical images of the abnormality, each medical image corresponding to an image of the abnormality acquired at a different time relative to a time of administration of a contrast medium, each medical image including a predetermined number of voxels; partitioning each medical image into at least two groups based on the obtained image data, wherein each group corresponds to a subset of the predetermined number of voxels, and each group is associated with a temporal image pattern in the plurality of medical images; selecting, from among the temporal patterns, an enhancement temporal pattern as representative of the abnormality; and determining, based on the selected temporal pattern, a medical state of the abnormality.
摘要翻译：一种用于分析医学图像的方法，系统和计算机软件产品，包括获得表示异常的多个医学图像的图像数据，每个医学图像对应于在相对于管理时间的不同时间获取的异常的图像每个医学图像包括预定数量的体素; 基于获得的图像数据将每个医学图像划分为至少两组，其中每组对应于预定数量的体素的子集，并且每组与多个医学图像中的时间图像图案相关联; 从时间模式中选择表示异常的增强时间模式; 以及基于所选择的时间模式来确定异常的医疗状态。

10. 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。

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