公开(公告)号：US4875165A

公开(公告)日：1989-10-17

申请号：US126266

申请日：1987-11-27

申请人： Laura E. Fencil ,  Charles E. Metz ,  Kunio Doi

发明人： Laura E. Fencil ,  Charles E. Metz ,  Kunio Doi

IPC分类号： G06T7/00

CPC分类号： G06T7/0075 ,  G06T2207/30101

摘要： A novel method for determination of 3-D structure in biplane angiography, including determining the distance of a perpendicular line from the focal spots of respective x-ray sources to respective image planes and defining the origin of each biplane image as the point of intersection with the perpendicular line thereto, obtaining two biplane digital images at arbitrary orientations with respect to an object, identifying at least 8 points in both images which correspond to respective points in the object, determining the image coordinates of the 8 or more identified object points in the respective biplane images, constructing a set of linear equations in 8 unknowns based on the image coordinates of the object points and based on the known focal spot to image plane distances for the two biplane images; solving the linear equations to yield the 8 unknowns, which represent the fundamental geometric parameters of the biplane imaging system; using the fundamental parameters to calculate the 3-dimensional positions of the object points identified in the biplane images; and determination of the 3-D positions of the vessel segments between the object points.

摘要翻译： 一种用于确定双平面血管造影术中3-D结构的新方法，包括确定垂直线与各个X射线源的焦点到各个图像平面的距离，并将每个双平面图像的原点定义为与 确定相对于对象的任意取向的两个双平面数字图像，识别与对象中的各个点对应的两个图像中的至少8个点，确定在该对象中的8个或更多个识别的对象点的图像坐标 基于对象点的图像坐标并基于已知的焦点到两个双平面图像的图像平面距离来构造8个未知数的一组线性方程; 求解线性方程得到8个未知数，表示双平面成像系统的基本几何参数; 使用基本参数来计算双平面图像中识别的对象点的三维位置; 以及在对象点之间确定血管段的3-D位置。

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：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：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。

公开(公告)号：US6058322A

公开(公告)日：2000-05-02

申请号：US900361

申请日：1997-07-25

申请人： Robert M. Nishikawa ,  Yulei Jiang ,  Kazuto Ashizawa ,  Kunio Doi

发明人： Robert M. Nishikawa ,  Yulei Jiang ,  Kazuto Ashizawa ,  Kunio Doi

IPC分类号： A61B6/00 ,  G06T1/00 ,  G06T7/00 ,  A61B5/05

CPC分类号： G06T7/0012 ,  Y10S128/925

摘要： A computer-aided method for detecting, classifying, and displaying candidate abnormalities, such as microcalcifications and interstitial lung disease in digitized medical images, such as mammograms and chest radiographs, a computer programmed to implement the method, and a data structure for storing required parameters, wherein in the classifying method candidate abnormalities in a digitized medical image are located, regions are generated around one or more of the located candidate abnormalities, features are extracted from at least one of the located candidate abnormalities within the region and from the region itself, the extracted features are applied to a classification technique, such as an artificial neural network (ANN) to produce a classification result (i.e., probability of malignancy in the form of a number and a bar graph), and the classification result is displayed along with the digitized medical image annotated with the region and the candidate abnormalities within the region. In the detecting method candidate abnormalities in each of a plurality of digitized medical images are located, regions around one or more of the located candidate abnormalities in each of a plurality of digitized medical images are generated, the plurality of digitized medical images annotated with respective regions and candidate abnormalities within the regions are displayed, and a first indicator (e.g., blue arrow) is superimposed over candidate abnormalities comprising of clusters and a second indicator (e.g., red arrow) is superimposed over candidate abnormalities comprising of masses. In a user modification mode, during classification, a user modifies the located candidate abnormalities, the determined regions, and/or the extracted features, so as to modify the extracted features applied to the classification technique and the displayed results, and, during detection, a user modifies the located candidate abnormalities, the determined regions, and the extracted features, so as to modify the displayed results.

公开(公告)号：US6011862A

公开(公告)日：2000-01-04

申请号：US098504

申请日：1998-06-17

申请人： Kunio Doi ,  Xin-Wei Xu ,  Shigehiko Katsuragawa ,  Junji Morishita

发明人： Kunio Doi ,  Xin-Wei Xu ,  Shigehiko Katsuragawa ,  Junji Morishita

IPC分类号： A61B6/00 ,  G06T1/00 ,  G06T7/00 ,  G06T7/60 ,  G06K9/62

CPC分类号： G06T7/0012

摘要： A computerized method for the detection and characterization of disease in an image derived from a chest radiograph, wherein an image in the chest radiograph is processed to determine the ribcage boundary, including lung top edges, right and left ribcage edges, and right and left hemidiaphragm edges. Texture measures including RMS variations of pixel values within regions of interest are converted to relative exposures and corrected for system noise existing in the system used to produce the image. Texture and/or geometric pattern indices are produced. A histogram(s) of the produced index (indices) is produced and values of the histograms) are applied as inputs to a trained artificial neural network, which classifies the image as normal or abnormal. In one embodiment, obviously normal and obviously abnormal images are determined based on the ratio of abnormal regions of interest to the total number of regions of interest in a rule-based method, so that only difficult cases to diagnose are applied to the artificial neural network.

摘要翻译： 一种用于检测和表征来自胸部X光照片的图像中的疾病的计算机化方法，其中处理胸部X光片中的图像以确定胸腔边界，包括肺顶缘，右和左胸廓边缘，以及右侧和左侧膈肌 边缘。 包括感兴趣区域内的像素值的RMS变化的纹理度量被转换为相对曝光并且对用于产生图像的系统中存在的系统噪声进行校正。 产生纹理和/或几何图案索引。 生成的索引（索引）的直方图（直方图的值）被应用为训练的人造神经网络的输入，其将图像分类为正常或异常。 在一个实施例中，基于基于规则的方法，基于感兴趣的异常区域与感兴趣区域总数的比率来确定明显的正常和明显异常的图像，使得仅将困难的诊断情况应用于人造神经网络 。

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：US5732697A

公开(公告)日：1998-03-31

申请号：US562188

申请日：1995-11-22

申请人： Wei Zhang ,  Kunio Doi

发明人： Wei Zhang ,  Kunio Doi

IPC分类号： A61B6/00 ,  G06F15/18 ,  G06N3/00 ,  G06N3/08 ,  G06Q50/00 ,  G06T1/40 ,  G06T7/00 ,  A61B5/00 ,  A61B5/05

CPC分类号： G06T7/0012

摘要： A computerized method and system using a shift-invariant artificial neural network (SIANN) for the quantitative analysis of image data. A series of digitized medical images are used to train an artificial neural network to differentiate between diseased and normal tissue. The sum of the weights in groups between layers is constrained to be substantially zero so as to avoid saturation of layers which would otherwise be saturated by low frequency background noise. The method and system also include utilizing training-free zones to exclude from training the center portions of microcalcifications in the digitized images. The method and system further include rule-based selection criteria for providing a more accurate diagnosis.

摘要翻译： 一种使用移位不变人工神经网络（SIANN）进行图像数据定量分析的计算机化方法和系统。 使用一系列数字化医学图像来训练人造神经网络来区分患病和正常组织。 层之间的组中的权重之和被约束为基本为零，以避免否则将被低频背景噪声饱和的层的饱和。 该方法和系统还包括利用无训练区域来排除在数字化图像中训练微钙化的中心部分。 该方法和系统还包括用于提供更准确的诊断的基于规则的选择标准。

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：US5150292A

公开(公告)日：1992-09-22

申请号：US428059

申请日：1989-10-27

申请人： Kenneth R. Hoffmann ,  Kunio Doi

发明人： Kenneth R. Hoffmann ,  Kunio Doi

IPC分类号： A61B5/026 ,  A61B6/00 ,  G06F19/00 ,  G06T7/00 ,  G06T7/20

CPC分类号： A61B6/481 ,  A61B6/504 ,  A61B6/507 ,  G06T7/0016 ,  G06T7/20 ,  G06T2207/30101

摘要： A method and system for quantitation of blood flow rates by using digital subtraction angiographic (DSA) images, wherein the spatial shift of the distribution of contrast material injected into an opacified vessel in the acquired angiographic images is analyzed as a bolus of the contrast material proceeds through the vessel. In order to determine the distance that the bolus travels between image acquisitions, there is obtained from the DSA images the distribution of vessel contrast along the length of the vessel, called and "distance-density" curve. The distance that the contrast material travels during the time between two images acquisitions is determined by means of cross correlation of the two respective distance-density curves. The flow rate between the image acquisitions is calculated by multiplying this distance by the frame rate and the vessel cross-sectional area which is estimated from the vessel size assuming a circular cross section. Thus, for high frame-rate acquistions, instantaneous blood flow rates can be determined. The method and system are particularly useful for measurement of pulsatile blood flow rates.

摘要翻译： 一种通过使用数字减影血管造影（DSA）图像来定量血流速度的方法和系统，其中分析所获取的血管造影图像中注入到不透明容器中的造影剂的分布的空间位移，作为造影材料进行的推注 通过船只。 为了确定推注在图像采集之间的距离，从DSA图像获得沿容器长度的血管对比度的分布，称为“距离密度”曲线。 通过两个相应的距离密度曲线的互相关来确定对比材料在两次图像采集期间的行进距离。 通过将该距离乘以帧速率和从容器尺寸估计的容器横截面面积来计算图像采集之间的流量，其假设为圆形横截面。 因此，对于高帧速率采集，可以确定瞬时血流速率。 该方法和系统对于测量脉动血流速度特别有用。