公开(公告)号：US5974165A

公开(公告)日：1999-10-26

申请号：US523210

申请日：1995-09-05

申请人： Maryellen L. Giger ,  Chin-Tu Chen ,  Samuel Armato ,  Kunio Doi

发明人： Maryellen L. Giger ,  Chin-Tu Chen ,  Samuel Armato ,  Kunio Doi

IPC分类号： G01N23/04 ,  A61B6/00 ,  G06T1/00 ,  G06T3/00 ,  G06T5/00 ,  G06T7/60 ,  G06K9/36 ,  G06T5/40

CPC分类号： G06T7/0012 ,  G06T3/0075 ,  G06T7/0083 ,  G06T7/0091 ,  G06T7/606 ,  G06T2207/10116 ,  G06T2207/30061

摘要： A method and system for the computerized registration of radionuclide images with radiographic images, including generating image data from radiographic and radionuclide images of the thorax. Techniques include contouring the lung regions in each type of chest image, scaling and registration of the contours based on location of lung apices, and superimposition after appropriate shifting of the images. Specific applications are given for the automated registration of radionuclide lungs scans with chest radiographs. The method in the example given yields a system that spatially registers and correlates digitized chest radiographs with V/Q scans in order to correlate V/Q functional information with the greater structural detail of chest radiographs. Final output could be the computer-determined contours from each type of image superimposed on any of the original images, or superimposition of the radionuclide image data, which contains high activity, onto the radiographic chest image.

摘要翻译： 一种用放射性图像计算机化注册放射线图像的方法和系统，包括从放射照相和胸部放射性核素图像生成图像数据。 技术包括在每种类型的胸部图像中轮廓化肺部区域，基于肺顶点的位置缩放和对准轮廓，以及在适当移动图像之后叠加。 给出了用于放射性核素肺扫描的胸部X光照片的自动登记的具体应用。 在给出的示例中的方法产生一个系统，其将数字化胸部X光片与V / Q扫描空间寄存和相关联，以便将V / Q功能信息与胸部X光片的较大结构细节相关联。 最终输出可以是来自叠加在任何原始图像上的每种类型的图像的计算机确定的轮廓，或者包含高活性的放射性核素图像数据叠加到放射照相胸部图像上。

公开(公告)号：US06205348B1

公开(公告)日：2001-03-20

申请号：US09298852

申请日：1999-04-26

申请人： Maryellen L. Giger ,  Kunio Doi

发明人： Maryellen L. Giger ,  Kunio Doi

IPC分类号： A61B505

CPC分类号： G06T7/0012 ,  A61B6/4042 ,  A61B6/482 ,  A61B6/505 ,  A61B6/583 ,  G06T2207/10116 ,  G06T2207/30008 ,  G06T2207/30012

摘要： A computerized method and system for the radiographic analysis of bone structure and risk of future fracture with or without the measurement of bone mass. Techniques including texture analysis for use in quantitating the bone structure and risk of future fracture. The texture analysis of the bone structure incorporates directionality information, for example in terms of the angular dependence of the RMS variation and first moment of the power spectrum of a ROI in the bony region of interest. The system also includes using dual energy imaging in order to obtain measures of both bone mass and bone structure with one exam. Specific applications are given for the analysis of regions within the vertebral bodies on conventional spine radiographs. Techniques include novel features that characterize the power spectrum of the bone structure and allow extraction of directionality features with which to characterize the spatial distribution and thickness of the bone trabeculae. These features are then merged using artificial neural networks in order to yield a likelihood of risk of future fracture. In addition, a method and system is presented in which dual-energy imaging techniques are used to yield measures of both bone mass and bone structure with one low-dose radiographic examination; thus, making the system desirable for screening (for osteoporosis and risk of future fracture).

摘要翻译： 一种计算机化方法和系统，用于骨骼结构的射线照相分析和未来骨折的风险，有或没有骨量的测量。 包括用于量化骨骼结构和未来骨折风险的纹理分析的技术。 骨结构的纹理分析包括方向性信息，例如在感兴趣的骨区域中的ROI的RMS变化和ROI的功率谱的第一时刻的角度依赖性方面。 该系统还包括使用双能量成像，以便通过一次检查获得骨量和骨骼结构的测量。 给出了常规脊柱X光照片对椎体内部区域进行分析的具体应用。 技术包括表征骨骼结构的功率谱的新特征，并且允许提取用于表征骨小梁的空间分布和厚度的方向性特征。 然后使用人工神经网络将这些特征合并，以产生未来骨折风险的可能性。 此外，提出了一种方法和系统，其中使用双能量成像技术通过一次低剂量射线照相检查来产生骨量和骨结构的测量; 因此，使得该系统对于筛选（对于骨质疏松症和未来骨折的风险）是理想的。

公开(公告)号：US5289374A

公开(公告)日：1994-02-22

申请号：US843715

申请日：1992-02-28

申请人： Kunio Doi ,  Tsuneo Matsumoto ,  Maryellen L. Giger ,  Akiko Kano

发明人： Kunio Doi ,  Tsuneo Matsumoto ,  Maryellen L. Giger ,  Akiko Kano

IPC分类号： A61B6/00 ,  G06F19/00 ,  G06T7/00 ,  G06F15/42 ,  G06F15/70 ,  G06K9/46

CPC分类号： G06T7/0012 ,  G06F19/345 ,  G06K9/346 ,  G06F19/321 ,  G06K2209/05 ,  G06T2207/30064

摘要： A computerized method and system for reducing the number of false-positive detections of nodule candidates in the detection of abnormalities in digital chest radiography. The image is initially subjected to an image difference technique where the detection sensitivity is increased so as to avoid missing small nodules which might otherwise go undetected. Such a technique tends to increase the number of false-positives, however, leading to possible incorrect diagnoses of the radiographs. To reduce the number of false-positives, feature extraction techniques are applied to grown regions around the nodule candidates, in order to provide computer generated information concerning the candidates. A data base of parameters common to false-positives is compared to calculated parameters of a candidate of interest. The candidates with grown region parameters within the data base range common to false-positives are eliminated as being probable false-positive detections due to normal background anatomical features.

摘要翻译： 一种计算机化方法和系统，用于在数字胸部X线检查异常检测中减少结节候选者的假阳性检测次数。 图像最初经受图像差异技术，其中检测灵敏度增加，以避免丢失可能未被检测到的小结节。 这种技术倾向于增加假阳性的数量，然而，导致可能的错误诊断的X光片。 为了减少假阳性的数量，将特征提取技术应用于结核候选者周围的生长区域，以便提供关于候选者的计算机产生的信息。 将假阳性共同参数的数据库与感兴趣的候选者的计算参数进行比较。 具有假阳性共同的数据库范围内生长区域参数的候选者被消除为由于正常背景解剖特征引起的可能的假阳性检测。

公开(公告)号：US5881124A

公开(公告)日：1999-03-09

申请号：US220917

申请日：1994-03-31

申请人： Maryellen L. Giger ,  Kyongtae Ty Bae ,  Kunio Doi

发明人： Maryellen L. Giger ,  Kyongtae Ty Bae ,  Kunio Doi

IPC分类号： A61B6/03 ,  G06T1/00 ,  G06T7/00

CPC分类号： G06T7/0012 ,  G06T2207/10081 ,  G06T2207/30064 ,  Y10S378/901

摘要： A method and system for the automated detection of lesions in computed tomographic images, including generating image data from at least one selected portion of an object, for example, from CT images of the thorax. The image data are then analyzed in order to produce the boundary of the thorax. The image data within the thoracic boundary is then further analyzed to produce boundaries of the lung regions using predetermined criteria. Features within the lung regions are then extracted using multi-gray-level thresholding and correlation between resulting multi-level threshold images and between at least adjacent sections. Classification of the features as abnormal lesions or normal anatomic features is then performed using geometric features yielding a likelihood of being an abnormal lesion along with its location in either the 2-D image section or in the 3-D space of the object.

摘要翻译： 一种用于自动检测计算机断层图像中的病变的方法和系统，包括从诸如胸部的CT图像的对象的至少一个选定部分生成图像数据。 然后分析图像数据以产生胸部的边界。 然后进一步分析胸部边界内的图像数据，以使用预定标准产生肺部区域的边界。 然后使用多灰度阈值和所得到的多级阈值图像之间和至少相邻部分之间的相关性来提取肺区域内的特征。 然后使用几何特征进行特征作为异常损伤或正常解剖特征的分类，其产生可能性为异常损伤的可能性以及其位于对象的2D图像部分或3D图像部分中的位置。

公开(公告)号：US5537485A

公开(公告)日：1996-07-16

申请号：US915631

申请日：1992-07-21

申请人： Robert M. Nishikawa ,  Maryellen L. Giger ,  Kunio Doi

发明人： Robert M. Nishikawa ,  Maryellen L. Giger ,  Kunio Doi

IPC分类号： A61B6/00 ,  G06T7/00 ,  G06K9/00

CPC分类号： G06T7/0012 ,  G06T2207/30068

摘要： A method of computerized detection of clustered microcalcifications in digital mammograms, including obtaining a digitized mammogram, deriving a difference image signal from the digitized mammogram, performing global grey-level thresholding, area filtering, and local grey-level thresholding on the difference image, in that order, performing a texture discrimination of the signal extracted from the previous step, performing a cluster filtering technique on the texture discriminated signals to identify locations in the digitized mammogram corresponding to candidate clustered microcalcifications, performing a feature extraction step in which the area, contrast and background pixel values of signals corresponding to the candidate clustered microcalcifications in the original image are extracted, performing thresholding tests based on the extracted features to eliminate from the candidate clustered microcalcifications particular candidate clustered microcalcification identified as corresponding to false-positive identifications, cluster filtering the remaining candidate clustered microcalcifications to eliminate further candidate clustered microcalcifications which are not sufficiently clustered, and outputting to a radiologist an image indicating, by use of arrows, the positions of the remaining clustered microcalcifications.

摘要翻译： 一种在数字乳腺X线照片中计算机化检测聚类微钙化的方法，包括获得数字化乳腺X线照片，从数字化乳房X光检查图导出差分图像信号，对差异图像执行全局灰度阈值处理，区域滤波和局部灰度阈值处理， 执行从前一步骤提取的信号的纹理鉴别，对纹理鉴别信号执行群集滤波技术以识别对应于候选聚类微钙化的数字化乳房X线照片中的位置，执行特征提取步骤，其中区域，对比度 并且提取与原始图像中的候选聚类微量化相对应的信号的背景像素值，基于所提取的特征进行阈值测试，以从候选聚类微量化中消除被识别为对应的特定候选聚类微钙化 对于假阳性识别，聚类过滤剩余的候选聚类微钙化以消除未充分聚集的进一步的候选聚类微钙化，并且向放射科医师输出使用箭头指示剩余聚类微钙化位置的图像。

公开(公告)号：US5133020A

公开(公告)日：1992-07-21

申请号：US383097

申请日：1989-07-21

申请人： Maryellen L. Giger ,  Kunio Doi ,  Charles E. Metz ,  Fang-Fang Yin

发明人： Maryellen L. Giger ,  Kunio Doi ,  Charles E. Metz ,  Fang-Fang Yin

IPC分类号： A61B6/00 ,  G06T1/00 ,  G06T7/00

CPC分类号： A61B6/502 ,  G06T7/0012 ,  G06T2207/30068

摘要： A method for automated analysis of abnormalities in the form of lesions and parenchymal distortions using digital images, including generating image data from respective of digital images derived from at least one selected portion of an object, for example, from mammographical digital images of the left and right breasts. The image data from each of the digital images are then correlated to produce correlated data in which normal anatomical structured background is removed. The correlated data is then searched using one or more predetermined criteria to identify in at least one of the digital images an abnormal region represented by a portion of the correlated data which meets the predetermined criteria. The location of the abnormal region is then indicated, and the indicated location is then subjected to classification processing to determine whether or not the abnormal region is benign or malignant. Classification is performed based on the degree of spiculations of the identified abnormal region. In order to enhance the process of searching for abnormal regions, in one embodiment the gray-level frequency-distributions of two or more images are matched by matching the cumulative gray-level histograms of the images in question.

摘要翻译： 一种用于使用数字图像以损伤和实质变形形式的异常的自动分析的方法，包括从来自对象的至少一个选定部分的各个数字图像生成图像数据，例如从左侧的乳房摄影数字图像和 右乳房 然后将来自每个数字图像的图像数据相关联，以产生去除正常解剖结构背景的相关数据。 然后使用一个或多个预定标准来搜索相关数据，以便在至少一个数字图像中识别满足预定标准的相关数据的一部分所表示的异常区域。 然后指示异常区域的位置，然后对所指示的位置进行分类处理，以确定异常区域是否良性或恶性。 基于识别的异常区域的刺激程度进行分类。 为了增强搜索异常区域的处理，在一个实施例中，通过匹配所讨论的图像的累积灰度级直方图来匹配两个或多个图像的灰度级频率分布。

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。

公开(公告)号：US5931780A

公开(公告)日：1999-08-03

申请号：US158388

申请日：1993-11-29

申请人： Maryellen L. Giger ,  Kunio Doi

发明人： Maryellen L. Giger ,  Kunio Doi

IPC分类号： A61B6/00 ,  G06F19/00 ,  G06T7/00 ,  A61B5/05

CPC分类号： G06T7/0012 ,  A61B6/482 ,  A61B6/505 ,  A61B6/583 ,  A61B6/4042 ,  G06T2207/10116 ,  G06T2207/30008 ,  G06T2207/30012

摘要： A computerized method and system for the radiographic analysis of bone structure and risk of future fracture with or without the measurement of bone mass. Techniques including texture analysis for use in quantitating the bone structure and risk of future fracture. The texture analysis of the bone structure incorporates directionality information, for example in terms of the angular dependence of the RMS variation and first moment of the power spectrum of a ROI in the bony region of interest. The system also includes using dual energy imaging in order to obtain measures of both bone mass and bone structure with one exam. Specific applications are given for the analysis of regions within the vertebral bodies on conventional spine radiographs. Techniques include novel features that characterize the power spectrum of the bone structure and allow extraction of directionality features with which to characterize the spatial distribution and thickness of the bone trabeculae. These features are then merged using artificial neural networks in order to yield a likelihood of risk of future fracture. In addition, a method and system is presented in which dual-energy imaging techniques are used to yield measures of both bone mass and bone structure with one low-dose radiographic examination; thus, making the system desirable for screening (for osteoporosis and risk of future fracture).

摘要翻译： 一种计算机化方法和系统，用于骨骼结构的射线照相分析和未来骨折的风险，有或没有骨量的测量。 包括用于量化骨骼结构和未来骨折风险的纹理分析的技术。 骨结构的纹理分析包括方向性信息，例如在感兴趣的骨区域中的ROI的RMS变化和ROI的功率谱的第一时刻的角度依赖性方面。 该系统还包括使用双能量成像，以便通过一次检查获得骨量和骨骼结构的测量。 给出了常规脊柱X光照片对椎体内部区域进行分析的具体应用。 技术包括表征骨骼结构的功率谱的新特征，并且允许提取用于表征骨小梁的空间分布和厚度的方向性特征。 然后使用人工神经网络将这些特征合并，以产生未来骨折风险的可能性。 此外，提出了一种方法和系统，其中使用双能量成像技术通过一次低剂量射线照相检查来产生骨量和骨结构的测量; 因此，使得该系统对于筛选（对于骨质疏松症和未来骨折的风险）是理想的。

公开(公告)号：US4907156A

公开(公告)日：1990-03-06

申请号：US68221

申请日：1987-06-30

申请人： Kunio Doi ,  Heang-Ping Chan ,  Maryellen L. Giger

发明人： Kunio Doi ,  Heang-Ping Chan ,  Maryellen L. Giger

IPC分类号： G06T5/50 ,  G06T7/00

CPC分类号： G06T5/004 ,  G06T7/0002 ,  G06T7/0012 ,  G06T2207/30064

摘要： A method and system for detecting and displaying abnormal anatomic regions existing in a digital X-ray image, wherein a single projection digital X-ray image is processed to obtain signal-enhanced image data with a maximum signal-to-noise ratio (SNR) and is also processed to obtain signal-suppressed image data with a suppressed SNR. Then, difference image data are formed by subtraction of the signal-suppressed image data from the signal-enhanced image data to remove low-frequency structured anatomic background, which is basically the same in both the signal-suppressed and signal-enhanced image data. Once the structured background is removed, feature extraction, is performed. For the detection of lung nodules, pixel thresholding is performed, followed by circularity and/or size testing of contiguous pixels surviving thresholding. Threshold levels are varied, and the effect of varying the threshold on circularity and size is used to detect nodules. For the detection of mammographic microcalcifications, pixel thresholding and contiguous pixel area thresholding are performed. Clusters of suspected abnormalities are then detected.

摘要翻译： 一种用于检测和显示存在于数字X射线图像中的异常解剖区域的方法和系统，其中处理单个投影数字X射线图像以获得具有最大信噪比（SNR）的信号增强图像数据， 并且还被处理以获得具有抑制的SNR的信号抑制图像数据。 然后，通过从信号增强图像数据中减去信号抑制图像数据来形成差分图像数据，以去除信号抑制和信号增强图像数据中基本相同的低频结构解剖背景。 一旦删除结构化背景，就执行特征提取。 对于肺结节的检测，执行像素阈值处理，随后进行阈值处理的连续像素的圆度和/或尺寸测试。 阈值水平变化，并且使用改变阈值对圆度和大小的影响来检测结节。 为了检测乳房X线摄影微钙化，进行像素阈值和连续像素区域阈值处理。 然后检测到疑似异常的群集。

公开(公告)号：US6075878A

公开(公告)日：2000-06-13

申请号：US979623

申请日：1997-11-28

申请人： Hiroyuki Yoshida ,  Wei Zhang ,  Robert M. Nishikawa ,  Kunio Doi

发明人： Hiroyuki Yoshida ,  Wei Zhang ,  Robert M. Nishikawa ,  Kunio Doi

IPC分类号： G06F17/14 ,  G06T7/00 ,  G06K9/00

CPC分类号： G06F17/148 ,  G06T7/0012

摘要： A computer-aided diagnosis (CAD) method for detection of clustered microcalcifications in digital mammograms based on an image reconstruction using a substantially optimally weighted wavelet transform. Weights at individual scales of the wavelet transform are optimized based on a supervised learning method. In the learning method, an error function represents a difference between a desired output and a reconstructed image obtained from weighted wavelet coefficients of the wavelet transform for a given mammogram. The error function is then minimized by modifying the weights by means of a conjugate gradient algorithm. Performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis which indicated that the present invention outperformed both a difference-image technique and partial reconstruction method currently used in CAD methods.

摘要翻译： 一种用于基于使用基本上最佳加权的小波变换的图像重建来检测数字乳腺X线照片中的聚类微钙化的计算机辅助诊断（CAD）方法。 基于监督学习方法优化小波变换的个体尺度权重。 在学习方法中，误差函数表示对于给定的乳腺X线照片，从小波变换的加权小波系数获得的期望输出和重建图像之间的差。 然后通过使用共轭梯度算法修改权重来最小化误差函数。 通过接收机操作特性（ROC）分析评估了最佳加权小波的性能，表明本发明优于当前在CAD方法中使用的差分图像技术和部分重建方法。