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1. 发明授权

US6011862A Computer-aided method for automated image feature analysis and diagnosis of digitized medical images 失效
标题翻译：计算机辅助方法用于数字化医学图像的自动图像特征分析和诊断
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。

2. 发明授权

US6088473A Method and computer readable medium for automated analysis of chest radiograph images using histograms of edge gradients for false positive reduction in lung nodule detection 失效
标题翻译：方法和计算机可读介质，用于使用边缘梯度的直方图自动分析胸部X光片图像，用于肺结核检测中的假阳性减少
公开(公告)号：US6088473A
公开(公告)日：2000-07-11
申请号：US27685
申请日：1998-02-23
申请人： Xin-Wei Xu , Kunio Doi
发明人： Xin-Wei Xu , Kunio Doi
IPC分类号： A61B6/00 , G06T1/00 , G06T7/00 , G06K9/62
CPC分类号： G06T7/0012
摘要： An automated method, and a computer storage medium storing instructions for executing the method, for analysis of image features in lung nodule detection in a chest radiographic image represented by digital data, including preprocessing the image to identify candidate nodules in the image; establishing a region of interest (ROI) including a candidate nodule identified in the preprocessing step; performing image enhancement of the candidate nodule within the ROI; obtaining a histogram of accumulated edge gradients as a function of radial angles withing the ROI after performing the image enhancement; and determining whether the candidate nodule is a false positive based on the obtained histogram. A 64.times.64-pixel region of interest (ROI) centered at the candidate location is used. The contrast of the ROI is improved by a two-dimensional background subtraction. A nodule shape matched filter is used for enhancement of the nodular pattern located in the central area of the ROI. Analysis of the histogram resulted in identification of seven features, including (1) a maximum histogram value, (2) a minimum histogram value, (3) a partial average value of the histogram, (4) a standard deviation of the histogram values near the radial axis, (5) a partial standard deviation of histogram values, (6) a width of the histogram including both sides from zero degrees of the radial angle, at a predetermined histogram value, and (7) a ratio of a maximum histogram value near the radial axis to a maximum histogram value in two predetermined outside ranges of the radial axis, useful for the identification and elimination of false positives.
摘要翻译：一种自动化方法和存储用于执行该方法的指令的计算机存储介质，用于分析由数字数据表示的胸部放射照相图像中的肺结节检测中的图像特征，包括预处理图像以识别图像中的候选结节; 建立包括在所述预处理步骤中识别的候选结节的感兴趣区域（ROI）; 在ROI内执行候选结节的图像增强; 在执行图像增强之后，获得作为具有ROI的径向角的函数的累积边缘梯度的直方图; 以及基于所获得的直方图来确定候选结节是否为假阳性。使用以候选位置为中心的64×64像素的感兴趣区域（ROI）。通过二维背景减法来提高ROI的对比度。结节形状匹配滤波器用于增强位于ROI中心区域的结节图案。直方图的分析得出了七个特征的识别，包括（1）最大直方图值，（2）最小直方图值，（3）直方图的部分平均值，（4）直方图值的标准偏差近径向轴，（5）直方图值的部分标准偏差，（6）直方图的宽度，包括在径向角的零度的两侧，以预定的直方图值，以及（7）最大直方图的比率径向轴附近的值与径向轴的两个预定的外部范围内的最大直方图值有用，用于识别和消除假阳性。

3. 发明授权

US6141437A CAD method, computer and storage medium for automated detection of lung nodules in digital chest images 失效
标题翻译： CAD方法，用于数字胸部图像中肺结节自动检测的计算机和存储介质
公开(公告)号：US6141437A
公开(公告)日：2000-10-31
申请号：US562087
申请日：1995-11-22
申请人： Xin-Wei Xu , Kunio Doi
发明人： Xin-Wei Xu , Kunio Doi
IPC分类号： A61B6/00 , G06F15/18 , G06N3/00 , G06Q50/00 , G06T1/00 , G06T7/00 , G06K9/80
CPC分类号： G06T7/0012 , Y10S128/922 , Y10S128/925
摘要： A computer-aided diagnosis (CAD) method for the automated detection of lung nodules in a digital chest image, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule candidates are first automatically selected by thresholding the difference image and then classified in six groups. A large number of false positives are eliminated by adaptive rule-based tests applied to the original chest image and in the difference image and an artificial neural network (ANN) applied to remaining candidate nodule locations in the original chest image. Using two hundred PA chest radiographs, 100 normal and 100 abnormal, as the database, the presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. The CAD method achieves, on average, the sensitivity of 70% at 1.7 false positives per chest image.
摘要翻译：用于自动检测数字胸部图像中的肺结节的计算机辅助诊断（CAD）方法，用于实现该方法的程序化计算机以及存储用于实现该方法的程序的存储介质，其中首先自动选择结节候选通过阈值差分图像，然后分为六组。通过适用于原始胸部图像和差异图像的适应性规则测试和应用于原始胸部图像中剩余候选结节位置的人造神经网络（ANN）来消除大量假阳性。使用200张PA胸片，100例正常和100例异常，作为数据库，100例异常病例中存在结节，由两名经验丰富的放射科医师根据CT扫描或放射照相随访确认。平均而言，CAD方法在每个胸部图像的1.7个假阳性下实现了70％的灵敏度。

4. 发明授权

US06240201B1 Computerized detection of lung nodules using energy-subtracted soft-tissue and standard chest images 失效
标题翻译：使用能量减去的软组织和标准胸部图像计算机化检测肺结节
公开(公告)号：US06240201B1
公开(公告)日：2001-05-29
申请号：US09121719
申请日：1998-07-24
申请人： Xin-Wei Xu , Kunio Doi , Heber MacMahon
发明人： Xin-Wei Xu , Kunio Doi , Heber MacMahon
IPC分类号： G06K900
CPC分类号： G06T7/0012
摘要： A method, system and computer readable medium configured for computerized detection of lung abnormalities, including obtaining a standard digital chest image and a soft-tissue digital chest image; generating a first difference image from the standard digital chest image and a second difference image from the soft-tissue digital chest image; identifying candidate abnormalities in the first and second difference images; extracting from the standard digital chest image and the first difference image predetermined first features of each of the candidate abnormalities identified in the first difference image; extracting from the soft-tissue digital chest image and the second difference images predetermined second features of each of the candidate abnormalities identified in the second difference image; analyzing the extracted first features and the extracted second features to identify and eliminate false positive candidate abnormalities respectively corresponding thereto; applying extracted features from remaining candidate abnormalities derived respectively from the first and second difference images and remaining after the elimination of the false positive candidate abnormalities to respective artificial neural networks to eliminate further false positive candidate abnormalities; performing a logical OR operation of the candidate abnormalities derived respectively from the first and second difference images and remaining after the elimination of the false positive candidate abnormalities; and outputting a signal indicative of a result of performing the logical OR operation. The logical OR combination, of locations of the candidate abnormalities detected in the first difference image and the second difference image, yields an improved detection sensitivity (over 90%) and only slightly increased false positives rate (3.2 false positives per chest image).
摘要翻译：一种用于计算机化检测肺异常的方法，系统和计算机可读介质，包括获得标准数字胸部图像和软组织数字胸部图像; 从所述标准数字胸部图像生成第一差异图像和从所述软组织数字胸部图像产生第二差异图像; 识别第一和第二差异图像中的候选异常; 从所述标准数字胸部图像中提取所述第一差异图像中的每个所述候选异常的所述第一差分图像预定的第一特征; 从所述软组织数字胸部图像中提取第二差异图像和所述第二差异图像，在所述第二差异图像中识别的每个候选异常的预定第二特征; 分析提取的第一特征和提取的第二特征以识别和消除分别对应的假阳性候选异常; 从分别来自第一和第二差异图像的剩余候选异常中应用提取的特征，并且在将假阳性候选异常消除到各个人造神经网络之后保留以消除进一步的假阳性候选异常; 执行分别从第一和第二差分图像导出的候选异常的逻辑或运算，并且在消除假阳性候选异常之后保留; 并输出表示执行逻辑或运算的结果的信号。在第一差异图像和第二差异图像中检测到的候选异常的位置的逻辑OR组合产生改善的检测灵敏度（超过90％），并且仅仅稍微增加假阳性率（每个胸部图像为3.2个假阳性）。

5. 发明授权

US5790690A Computer-aided method for automated image feature analysis and diagnosis of medical images 失效
标题翻译：计算机辅助方法，用于医学图像的自动图像特征分析和诊断
公开(公告)号：US5790690A
公开(公告)日：1998-08-04
申请号：US428867
申请日：1995-04-25
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 , G06K9/00 , G06F15/00 , G06K9/46 , H04N1/40
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 histogram(s) 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。产生生成的索引（索引）的直方图，并将直方图的值作为输入被应用于训练的人造神经网络，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。

6. 发明授权

US06363163B1 Method and system for the automated temporal subtraction of medical images 失效
标题翻译：医学图像自动化时间减法的方法和系统
公开(公告)号：US06363163B1
公开(公告)日：2002-03-26
申请号：US09027468
申请日：1998-02-23
申请人： Xin-Wei Xu , Kunio Doi
发明人： Xin-Wei Xu , Kunio Doi
IPC分类号： G06K900
CPC分类号： G06T5/50 , A61B6/027 , G06T7/254
摘要： Method and system for the detection of interval change in medical images. Three dimensional images, such as previous and current section images in CT scans, are obtained. An anatomic feature, such as the lungs, is used to select sections containing lung by a gray-level thresholding technique. The section correspondence between the current and previous scans is determined automatically. The initial registration of the corresponding sections in the two scans is achieved by a rotation correction and a cross-correlation technique. A more accurate registration between the corresponding current and previous section images is achieved by local matching. A nonlinear warping process which is also based on the cross-correlation technique is applied to the previous image to yield a warped image after the matching. The final subtracted section images were derived by subtracting of the previous section images from the corresponding current section images. Interval changes such as a change in tumor size and a newly developed pleural effusion are enhanced significantly.
摘要翻译：用于检测医学图像间隔变化的方法和系统。获得三维图像，例如CT扫描中的先前和当前部分图像。解剖特征，如肺，用于通过灰度阈值技术选择含有肺的部位。自动确定当前扫描和以前扫描之间的部分对应关系。通过旋转校正和互相关技术来实现两次扫描中相应部分的初始配准。通过局部匹配实现相应的当前和前一个部分图像之间更准确的配准。还将基于互相关技术的非线性翘曲过程应用于先前的图像，以在匹配之后产生翘曲图像。通过从相应的当前部分图像中减去前一部分图像来导出最终减法部分图像。肿瘤大小变化和新发胸腔积液等间期变化明显增强。

7. 发明授权

US06205348B1 Method and system for the computerized radiographic analysis of bone 失效
标题翻译：骨的计算机化影像学分析方法与系统
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。

8. 发明授权

US6075878A Method for determining an optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms 失效
标题翻译：基于用于检测数字乳腺X线照片中微钙化的监督训练来确定最佳加权小波变换的方法
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。

9. 发明授权

US6058322A Methods for improving the accuracy in differential diagnosis on radiologic examinations 失效
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.

10. 发明授权

US5974165A Automated method and system for the alignment and correlation of images from two different modalities 失效
标题翻译：用于两种不同模式的图像对准和相关的自动方法和系统
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。

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