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1. 发明授权

US5657362A Automated method and system for computerized detection of masses and parenchymal distortions in medical images 失效
标题翻译：医学图像质量和实质扭曲的计算机化检测自动化方法和系统
公开(公告)号：US5657362A
公开(公告)日：1997-08-12
申请号：US393779
申请日：1995-02-24
申请人： Maryellen L. Giger , Ming Zhang , Ping Lu
发明人： Maryellen L. Giger , Ming Zhang , Ping Lu
IPC分类号： A61B6/00 , G06F19/00 , G06T1/00 , G06T5/00 , G06T7/00 , A61B6/04
CPC分类号： G06T7/0012 , G06T7/0081 , G06T2207/10116 , G06T2207/20061 , G06T2207/20132 , G06T2207/30068 , G06T2207/30096
摘要： A method and system for the automated detection of lesions such as masses and/or tissue (parenchymal) distortions in medical images such as mammograms. Dense regions and subcutaneous fat regions within a mammogram are segmented. A background correction may be performed within the dense regions. Hough spectrum within ROIs placed in the breast region of a mammogram are calculated and thresholded using the intensity value .eta. in order to increase sensitivity and reduce the number of false-positive detections. Lesions are detected based on the thresholded Hough spectra. The thresholded Hough spectra are also used to differentiate between benign and malignant masses.
摘要翻译：用于自动检测诸如乳房X线照相术的医学图像中的质量和/或组织（实质）变形的病变的方法和系统。分割乳腺X线照片内的密集区域和皮下脂肪区域。可以在密集区域内执行背景校正。使用强度值eta计算放置在乳房X线照片的乳房X射线照片区域内的霍夫谱，并使其阈值化，以增加灵敏度并减少假阳性检测的数量。基于阈值霍夫光谱检测病变。阈值霍夫光谱也用于区分良性和恶性肿块。

2. 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。

3. 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。

4. 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。

5. 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).

6. 发明授权

US5638458A Automated method and system for the detection of gross abnormalities and asymmetries in chest images 失效
标题翻译：自动化方法和系统，用于检测胸部图像的总体异常和不对称性
公开(公告)号：US5638458A
公开(公告)日：1997-06-10
申请号：US159133
申请日：1993-11-30
申请人： Maryellen L. Giger , Samuel Armato , Heber MacMahon
发明人： Maryellen L. Giger , Samuel Armato , Heber MacMahon
IPC分类号： A61B6/00 , G06T1/00 , G06T7/00 , G06T7/60 , G06K9/00
CPC分类号： G06T7/608 , G06T7/0012 , G06T2207/10116 , G06T2207/30061
摘要： A method for the automated detection of gross abnormalities and asymmetries in chest images, including generating image data from radiographic images of the thorax. The image data are then analyzed in order to produce the boundaries of the aerated lung regions in the thorax. This analysis comprises location of the mediastinum and lung apices, iterative global thresholding with centroid testing of contours, local thresholding on regions along initial contours of the aerated lung, correction for regions near the costo- and cardiophrenic angles in the chest, analysis of the areas and density distribution within the aerated lung regions in the chest and determination of the likelihood of the presence of a gross abnormality or asymmetry. Final output could be the computer determined contours of the lungs or the likelihood for abnormality.
摘要翻译：一种用于自动检测胸部图像的总体异常和不对称的方法，包括从胸部的放射照相图像生成图像数据。然后分析图像数据以产生胸部中充气肺区域的边界。该分析包括纵隔和肺顶点的位置，具有轮廓重心测试的迭代全局阈值，沿着充气肺初始轮廓的区域的局部阈值，胸部附近和心脏心脏角度附近的区域的校正，区域分析以及胸部充气肺部区域内的密度分布，并确定存在严重异常或不对称的可能性。最终输出可能是计算机确定的肺部轮廓或异常的可能性。

7. 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。

8. 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。

9. 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。

10. 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。

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