公开(公告)号：US06836558B2

公开(公告)日：2004-12-28

申请号：US09816217

申请日：2001-03-26

申请人： Kunio Doi ,  Hidetaka Arimura ,  Shigehiko Katsuragawa ,  Junji Morishita

发明人： Kunio Doi ,  Hidetaka Arimura ,  Shigehiko Katsuragawa ,  Junji Morishita

IPC分类号： G06K900

CPC分类号： G06K9/6203 ,  G06T7/0002 ,  G06T7/001 ,  G06T7/32 ,  G06T2207/30061

摘要： A method, system and computer readable medium for a computer-automated method for identifying given image data, including obtaining template image data corresponding to said given image data; calculating correlation values between the given image data and said template image data; and identifying said image data based on the correlation values calculated in the calculating step.

摘要翻译： 一种用于识别给定图像数据的计算机自动化方法的方法，系统和计算机可读介质，包括获得对应于所述给定图像数据的模板图像数据; 计算给定图像数据和所述模板图像数据之间的相关值; 以及基于在所述计算步骤中计算的相关值来识别所述图像数据。

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：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 。

公开(公告)号：US4851984A

公开(公告)日：1989-07-25

申请号：US81143

申请日：1987-08-03

申请人： Kunio Doi ,  Shigehiko Katsuragawa

发明人： Kunio Doi ,  Shigehiko Katsuragawa

IPC分类号： A61B6/00 ,  A61B5/08 ,  G06F19/00 ,  G06T1/00 ,  G06T5/00 ,  G06T7/00 ,  G06T7/40

CPC分类号： A61B5/08 ,  A61B6/5258 ,  G06T7/0012 ,  G06T7/0081 ,  A61B5/7257 ,  G06T2207/20144 ,  G06T2207/30061 ,  Y10S378/901

摘要： A method and system for automated analysis of digital radiographic images in which regions-of-interest (ROI's) are first determined, and subsequently analyzed for abnormalities. To locate the ROI's, the outer ribcage and midline boundary locations of the chest image are determined from the digital image data. Vertical profiles are then selected and background trend is then removed from each vertical profile. A shift-variant sinusoidal function is fitted to each vertical profile and ROI's are selected based on the fitted functions. The non-uniform background trend is removed from the original image data of each selected ROI to obtain corrected data. The power spectrum of the lung texture is obtained from the 2D Fourier transform of the corrected data and is filtered by the human visual response. Finally, the root-mean-square (rms) variation, R, and the first moment of the power spectrum, M, are determined as quantitative texture measures for the magnitude and coarseness (or fineness), respectively, of the lung texture.

摘要翻译： 一种用于自动分析数字放射照相图像的方法和系统，其中首先确定感兴趣区域（ROI），并随后分析异常。 为了定位ROI，从数字图像数据确定胸部图像的外部胸腔和中线边界位置。 然后选择垂直剖面，然后从每个垂直剖面中去除背景趋势。 根据拟合的功能，选择每个垂直剖面的偏移正弦函数，并选择ROI。 从每个所选ROI的原始图像数据中去除不均匀的背景趋势，以获得校正数据。 肺结构的功率谱由校正数据的二维傅里叶变换获得，并通过人类视觉反应过滤。 最后，均方根（rms）变化R和功率谱的第一时刻M分别被确定为肺结构的大小和粗糙度（或细度）的定量纹理测量。

公开(公告)号：US4839807A

公开(公告)日：1989-06-13

申请号：US126847

申请日：1987-11-27

申请人： Kunio Doi ,  Shigehiko Katsuragawa

发明人： Kunio Doi ,  Shigehiko Katsuragawa

IPC分类号： A61B5/08 ,  G06F19/00 ,  G06T5/00 ,  G06T7/00

CPC分类号： G06T7/0012 ,  A61B5/08 ,  A61B5/7264 ,  G06F19/321 ,  G06F19/324 ,  G06F19/345 ,  G06F19/3487 ,  G06T7/401 ,  G06T2207/30061 ,  Y10S378/901

摘要： A method and system for automated classification of distinction between normal lungs and abnormal lungs with interstitial disease, based on the analysis of predetermined physical texture measures and also on a data base for normal lungs of these texture measures. The texture measures selected are the RMS variation, R, and the first moment of power spectrum, M, for lung texture. These two texture measures are normalized by using the data base for normal lungs. A single texture index is determined from the two normalized texture measures by taking into account the distribution (or the data base) of texture measures obtained from abnormal lungs, in order to facilitate the automated classification of normal and abnormal lungs. A threshold texture index is then chosen for initial selection of "abnormal" regions of interest (ROIs), which contain a large texture index above the threshold level. The selected abnormal ROIs are then subjected to three independent tests for a (1) definitely abnormal singular pattern, (2) localized abnormal pattern for two or more abnormal clustered ROIs, and (3) diffuse abnormal pattern for more than four abnormal ROIs distributed through the lung. A chest image containing any one of these abnormal patterns is classified as showing an abnormal lung with interstitial disease.

摘要翻译： 基于对预定的物理纹理测量的分析，以及这些纹理测量的正常肺的数据库，自动分类正常肺和具有间质性疾病的异常肺之间的区别的方法和系统。 所选择的纹理度量是RMS变化，R和功率谱的第一时刻M，用于肺结构。 通过使用正常肺的数据库对这两种纹理测量进行归一化。 通过考虑从异常肺获得的纹理测量的分布（或数据库），从两个归一化纹理测量中确定单个纹理指数，以便于正常和异常肺的自动分类。 然后选择阈值纹理索引用于初始选择感兴趣的“异常”区域（ROI），其包含高于阈值水平的大的纹理指数。 然后对所选择的异常ROI进行三（1）个绝对异常奇异模式的独立测试，（2）两个或更多个异常聚类ROI的局部异常模式，以及（3）分布在四个异常ROI中的扩散异常模式 肺。 含有这些异常模式中的任何一种的胸部图像被分类为显示具有间质性疾病的异常肺。

公开(公告)号：US07043066B1

公开(公告)日：2006-05-09

申请号：US09830562

申请日：1999-11-05

申请人： Kunio Doi ,  Qiang Li ,  Shigehiko Katsuragawa ,  Takayuki Ishida

发明人： Kunio Doi ,  Qiang Li ,  Shigehiko Katsuragawa ,  Takayuki Ishida

IPC分类号： G06K9/00

CPC分类号： G06T5/50 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/174 ,  G06T7/33 ,  G06T2207/10116 ,  G06T2207/20224 ,  G06T2207/30064

摘要： A method, system and computer readable medium for computerized processing of chest images including obtaining a digital first image of a chest (S100); producing a second image which is a mirror image (S300) of the first image; performing image warping on one of the first and second images to produce a warped image (S400) which is registered to the other of the first and second images; and subtracting the warped image from the other image to generate a subtraction image (S600). Another embodiment includes obtaining a digital first image of the chest of a subject; detecting ribcage edges on both sides of the lungs in the first chest image; determining average horizontal locations of the left and right ribcage edges at plural vertical locations; fitting the determined average horizontal locations to a straight line to derive a midline; rotating the chest image so that the midline is vertical; and shifting the rotated image to produce a lateral inclination corrected (S200) second image with the midline centered in the lateral inclination corrected image.

摘要翻译： 一种用于胸部图像的计算机化处理的方法，系统和计算机可读介质，包括获得胸部的数字第一图像（S100）; 产生作为第一图像的镜像（S 300）的第二图像; 在第一和第二图像之一上执行图像扭曲以产生被注册到第一和第二图像中的另一个的翘曲图像（S 400）; 以及从所述另一图像中减去所述翘曲图像以生成减法图像（S 600）。 另一实施例包括获得对象胸部的数字第一图像; 在第一胸部图像中检测肺两侧的肋骨边缘; 确定在多个垂直位置处的左和右胸腔边缘的平均水平位置; 将确定的平均水平位置拟合到直线以导出中线; 旋转胸部图像，使中线垂直; 并且移动旋转的图像以产生以横向倾斜校正图像为中心的中线的横向倾斜校正（S 200）第二图像。

公开(公告)号：US20090074276A1

公开(公告)日：2009-03-19

申请号：US12233031

申请日：2008-09-18

申请人： Kunio Doi ,  Shigehiko Katsuragawa ,  Yoshinori Itai ,  Hyoungseop Kim

发明人： Kunio Doi ,  Shigehiko Katsuragawa ,  Yoshinori Itai ,  Hyoungseop Kim

IPC分类号： G06K9/00

CPC分类号： G06T5/50 ,  G06K9/6202 ,  G06K2209/05 ,  G06T2207/10081 ,  G06T2207/20012 ,  G06T2207/20224 ,  G06T2207/30004

摘要： A method for improving the alignment accuracy between different medical images may be disclosed. A warped or non-warped previous image and a warped or non-warped current image may include a plurality of respective previous and current basic units, for example, pixels in a 2-dimensional image or voxels in a 3-dimensional image. To ensure accurate registration between the previous and current images, a first basic unit from the previous image may be replaced by a second basic unit from the current image if the value of the first and second basic units are identical or nearly identical. The first and second basic units may be selected from a nearly-identical region or “kernel” within the previous and current images.

摘要翻译： 可以公开用于改善不同医学图像之间的对准精度的方法。 翘曲或未翘曲的先前图像和翘曲或未翘曲的当前图像可以包括多个相应的先前和当前基本单元，例如二维图像中的像素或三维图像中的体素。 为了确保先前图像和当前图像之间的精确对准，如果第一和第二基本单元的值相同或几乎相同，则来自前一图像的第一基本单元可以被来自当前图像的第二基本单元替换。 第一和第二基本单元可以从先前和当前图像中几乎相同的区域或“核”中选择。

公开(公告)号：US06678399B2

公开(公告)日：2004-01-13

申请号：US09990311

申请日：2001-11-23

申请人： Kunio Doi ,  Takayuki Ishida ,  Shigehiko Katsuragawa

发明人： Kunio Doi ,  Takayuki Ishida ,  Shigehiko Katsuragawa

IPC分类号： G06K900

CPC分类号： G06T5/30 ,  G06T5/50 ,  G06T7/11 ,  G06T2207/10081 ,  G06T2207/20224 ,  G06T2207/30064

摘要： A method, system, computer readable medium and apparatus for computerized detection of lung nodules in computer tomography images, by which mask images are created such that subtractions of the mask image from a targeted CT section image reveal or highlight small lung nodules in the target CT section. The mask image is created utilizing the targeted CT section image along with other CT section images generated from the same CT scan. Based on these other section CT images and the targeted CT section image, a mask image can be created that is very similar to the target CT section image, but without the presence of small lung nodules. When the mask image is subtracted from the targeted CT section image, the differences between the mask images and the CT section images reveal small lung nodules. The mask image may be created by linear interpolation or a morphological filtered image.

摘要翻译： 一种用于计算机化检测计算机断层摄影图像中的肺结节的方法，系统，计算机可读介质和装置，通过该方法创建掩模图像，使得来自目标CT部分图像的掩模图像的减法显示或突出显示目标CT中的小肺结节 部分。 使用目标CT部分图像以及从相同CT扫描生成的其他CT部分图像创建掩模图像。 基于这些其他部分CT图像和目标CT部分图像，可以创建与目标CT部分图像非常相似但不存在小肺结节的掩模图像。 当从目标CT部分图像中减去掩模图像时，掩模图像和CT部分图像之间的差异显示出小的肺结节。 掩模图像可以通过线性内插或形态滤波图像来创建。

公开(公告)号：US07236619B2

公开(公告)日：2007-06-26

申请号：US10357442

申请日：2003-02-04

申请人： Kunio Doi ,  Yoshikazu Uchiyama ,  Shigehiko Katsuragawa

发明人： Kunio Doi ,  Yoshikazu Uchiyama ,  Shigehiko Katsuragawa

IPC分类号： G06K9/00

CPC分类号： G06T7/0012 ,  G06F19/00 ,  G06T2207/30061

摘要： An automated computerized scheme for detection and characterization of diffuse lung diseases on high-resolution computed tomography (HRCT) images including obtaining image data including pixels of an organ; segmenting the image data into organ image data and non-organ image data; extracting predetermined features from the organ image data to produce a set of image features; comparing the set of image features against a reference set of organ image features containing image data known to correspond to normal and abnormal conditions; and producing a comparison result.

摘要翻译： 一种用于在高分辨率计算机断层摄影（HRCT）图像上检测和表征弥漫性肺部疾病的自动计算机化方案，包括获得包括器官的像素的图像数据; 将图像数据分割成器官图像数据和非器官图像数据; 从所述器官图像数据中提取预定特征以产生一组图像特征; 将图像特征集合与包含已知对应于正常和异常条件的图像数据的器官图像特征的参考集进行比较; 并产生比较结果。

公开(公告)号：US06683973B2

公开(公告)日：2004-01-27

申请号：US10231064

申请日：2002-08-30

申请人： Qiang Li ,  Shigehiko Katsuragawa ,  Kunio Doi

发明人： Qiang Li ,  Shigehiko Katsuragawa ,  Kunio Doi

IPC分类号： G06K900

CPC分类号： G06T7/0012

摘要： A method to determine whether a candidate abnormality in a medical digital image is an actual abnormality, a system which implements the method, and a computer readable medium which stores program steps to implement the method, wherein the method includes obtaining a medical digital image including a candidate abnormality; obtaining plural first templates and plural second templates respectively corresponding to predetermined abnormalities and predetermined non-abnormalities; comparing the candidate abnormality with the obtained first and second templates to derive cross-correlation values between the candidate abnormality and each of the obtained first and second templates; determining the largest cross-correlation value derived in the comparing step and whether the largest cross-correlation value is produced by comparing the candidate abnormality with the first templates or with the second templates; and determining the candidate abnormality to be an actual abnormality when the largest cross-correlation value is produced by comparing the candidate abnormality with the first templates and determining the candidate abnormality to be a non-abnormality when the largest cross-correlation value is produced by comparing the candidate abnormality with the second templates. An actual abnormality is similarly classified as malignant or benign based on further cross-correlation values obtained by comparisons with additional templates corresponding to malignant and benign abnormalities.

摘要翻译： 一种确定医疗数字图像中的候选异常是否为实际异常的方法，实现该方法的系统以及存储执行该方法的程序步骤的计算机可读介质，其中，所述方法包括：获得包括 候选异常; 分别对应于预定的异常和预定的非异常获得多个第一模板和多个第二模板; 将所述候选异常与所获得的第一和第二模板进行比较，以导出候选异常与获得的第一和第二模板中的每一个之间的互相关值; 确定在比较步骤中导出的最大互相关值，以及通过将候选异常与第一模板或第二模板进行比较来产生最大互相关值; 以及当通过将所述候选异常与所述第一模板进行比较而产生所述最大互相关值时，将所述候选异常判定为实际异常，并且当通过比较产生所述最大互相关值时将所述候选异常确定为非异常 候选异常与第二个模板。 基于通过与对应于恶性和良性异常的附加模板的比较获得的进一步互相关值，实际异常类似地分类为恶性或良性。