公开(公告)号：US5343390A

公开(公告)日：1994-08-30

申请号：US843721

申请日：1992-02-28

申请人： Kunio Doi ,  Xuan Chen ,  Shigehiko Katsuragawa

发明人： Kunio Doi ,  Xuan Chen ,  Shigehiko Katsuragawa

IPC分类号： A61B6/00 ,  G06F19/00 ,  G06T5/00 ,  G06T7/00 ,  G06F15/00 ,  G06F15/42 ,  H04N5/30 ,  H04N1/40

CPC分类号： G06T7/0012 ,  G06F19/345 ,  G06T7/0083 ,  G06F19/321 ,  G06T2207/10116 ,  G06T2207/20132 ,  G06T2207/30061 ,  Y10S378/901

摘要： An automated method and system for discriminating between normal lungs and abnormal lungs having interstitial disease and/or septal lines, wherein a large number of adjacent regions of interest (ROIs) are selected, corresponding to an area on a digital image of a patient's lungs. The ROIs each contain a number of square or rectangular pixel arrays and are selected to sequentially fill in the total selected area of the lungs to be analyzed. A background trend is removed from each individual ROI and the ROIs are then analyzed to determine those exhibiting sharp edges, i.e., high edge gradients. A percentage of these sharp-edged ROIs are removed from the original sample based on the edge gradient analysis, a majority of which correspond to rib-edge containing ROIs. After removal of the sharp-edged ROIs, texture measurements are taken on the remaining sample in order to compare such data with predetermined data for normal and abnormal lungs. Thus, a computerized scheme for quantitative analysis of interstitial lung diseases and/or septal lines appearing in digitized chest radiographs can be implemented in practical clinical situations.

摘要翻译： 一种用于区分正常肺和具有间质疾病和/或间隔线的异常肺的自动化方法和系统，其中选择大量相关感兴趣区域（ROI），对应于患者肺部数字图像上的区域。 ROI各自包含多个正方形或矩形像素阵列，并且被选择以顺序地填充待分析的肺的总选择区域。 从每个单独的ROI中去除背景趋势，然后分析ROI以确定呈现锋利边缘即高边缘渐变的那些。 基于边缘梯度分析，这些锐利ROI的百分比从原始样本中移除，其中大部分对应于包含边缘边缘的ROI。 在清除锐利的ROI之后，对剩余的样本进行纹理测量，以便将这些数据与正常和异常肺的预定数据进行比较。 因此，可以在实际的临床情况下实施用于数字化胸片中出现的间质性肺病和/或间隔线的定量分析的计算机化方案。

公开(公告)号：US5319549A

公开(公告)日：1994-06-07

申请号：US981526

申请日：1992-11-25

申请人： Shigehiko Katsuragawa ,  Kunio Doi

发明人： Shigehiko Katsuragawa ,  Kunio Doi

IPC分类号： A61B6/00 ,  G06T5/00 ,  G06T7/00 ,  G06F15/00 ,  G06K9/00

CPC分类号： G06T7/0012 ,  G06T7/0083 ,  G06T2207/20192 ,  G06T2207/30061

摘要： A computerized method and system based on quantitative analysis of geometric features of various infiltrate patterns in chest images for the detection and categorization of abnormalities related to the infiltrate patterns. Chest images are digitized and a lung texture analysis is performed on a number of small regions of interest (ROIs) in order to determine a classification of normal or abnormal of the particular patient's lungs. If the lungs are determined as being abnormal, large ROIs with a 128.times.128 matrix are selected in order to cover the detected areas of abnormality. Overall background trend correction is then performed in these large ROIs using a 2D-surface fitting technique for isolation of the fluctuating patterns of the underlying lung texture. Opacities of interstitial infiltrates are identified from two processed images which are obtained by employing thresholding with a morphological filter and a line enhancement filter. Finally, ROIs are classified into nodular, reticular and/or reticulo-nodular patterns by measurement of parameters corresponding to the type of the abnormality pattern detected.

摘要翻译： 一种基于胸部图像各种渗透模式几何特征定量分析的计算机化方法和系统，用于检测和分类与渗透模式有关的异常。 胸部图像被数字化，并且对多个小的感兴趣区域（ROI）执行肺部纹理分析，以便确定特定患者肺的正常或异常的分类。 如果肺被确定为异常，则选择具有128×128矩阵的大的ROI以覆盖检测到的异常区域。 然后使用2D表面拟合技术在这些大的ROI中进行总体背景趋势校正，以分离潜在的肺结构的波动模式。 从通过使用形态滤波器和线增强滤波器的阈值获得的两个处理图像来识别间质浸润的不透明度。 最后，通过测量对应于检测到的异常模式的类型的参数，将ROI分类为结节状，网状和/或网状结节型。

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。

公开(公告)号：US06594378B1

公开(公告)日：2003-07-15

申请号：US09692218

申请日：2000-10-20

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： G06T3/0068 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/12 ,  G06T7/149 ,  G06T7/174 ,  G06T7/344 ,  G06T2207/20061 ,  G06T2207/30008 ,  G06T2207/30061 ,  Y10S128/922

摘要： A method, system and computer readable medium of computerized processing of chest images including obtaining digital first and second images of a chest and detecting rib edges in at least one of the first and second images. The rib edges are detected by correlating points in the at least one of the first and second images to plural rib edge models using a Hough transform to identify approximate rib edges in one of the images, and delineating actual rib edges derived from the identified approximate rib edges using a snake model. The method system and computer readable medium further include deriving the shift values using the actual rib edges and warping one of the first and second images to produce a warped image which is registered to the other of the first and second images based at least in part on the shift values.

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和第二图像以及检测第一和第二图像中的至少一个中的肋边缘。 通过使用霍夫变换将第一和第二图像中的至少一个图像中的点与多个肋边缘模型相关联来检测肋边缘，以识别其中一个图像中的近似肋边缘，并且描绘从识别的近似肋导出的实际肋边缘 边缘使用蛇模型。 方法系统和计算机可读介质进一步包括使用实际的肋边缘导出移位值，并使第一和第二图像之一变形，以产生至少部分地基于第一和第二图像被注册到第一和第二图像中的另一个的扭曲图像 移位值。

公开(公告)号：US06470092B1

公开(公告)日：2002-10-22

申请号：US09716335

申请日：2000-11-21

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。

公开(公告)号：US6078680A

公开(公告)日：2000-06-20

申请号：US900191

申请日：1997-07-25

申请人： Hiroyuki Yoshida ,  Shigehiko Katsuragawa ,  Yali Amit ,  Kunio Doi

发明人： Hiroyuki Yoshida ,  Shigehiko Katsuragawa ,  Yali Amit ,  Kunio Doi

IPC分类号： G06K9/64 ,  G06T7/00 ,  G06K9/00

CPC分类号： G06K9/6206 ,  G06T7/0012

摘要： A method and apparatus for discrimination of nodules and false positives in digital chest radiographs, using a wavelet snake technique. The wavelet snake is a deformable contour designed to identify the boundary of a relatively round object. The shape of the snake is determined by a set of wavelet coefficients in a certain range of scales. Portions of the boundary of a nodule are first extracted using a multiscale edge representation. The multiscale edges are then fitted by a gradient descent procedure which deforms the shape of a wavelet snake by changing its wavelet coefficients. The degree of overlap between the fitted snake and the multiscale edges is calculated and used as a fit quality indicator for discrimination of nodules and false detections.

摘要翻译： 使用小波蛇技术在数字胸片中鉴别结节和假阳性的方法和装置。 小波蛇是一种可变形轮廓，用于识别相对圆形物体的边界。 蛇的形状由一定范围的小波系数确定。 首先使用多尺度边缘表示提取结节边界的部分。 然后通过梯度下降过程来拟合多尺度边缘，该过程通过改变其小波系数来变形小波蛇的形状。 计算拟合蛇和多尺度边缘之间的重叠程度，并将其用作辨别结节和假检测的拟合质量指标。

公开(公告)号：US5982915A

公开(公告)日：1999-11-09

申请号：US900362

申请日：1997-07-25

申请人： Kunio Doi ,  Takayuki Ishida ,  Shigehiko Katsuragawa

发明人： Kunio Doi ,  Takayuki Ishida ,  Shigehiko Katsuragawa

IPC分类号： A61B6/00 ,  G06T1/00 ,  G06T3/00 ,  G06T5/50 ,  G06T7/00 ,  G06T7/60 ,  G06K9/00

CPC分类号： G06T7/003 ,  G06T3/0081 ,  G06T5/50 ,  G06T7/0016 ,  G06T7/2053 ,  G06T2207/10116 ,  G06T2207/30061

摘要： A method and computerized automated initial image matching technique for enhancing detection of interval changes between temporally subsequent radiographic images via image subtraction. The method includes the steps of digitizing images, normalizing density and contrast in the digital images, correcting for lateral inclination in the digital images, detecting edges of a same feature in each image, converting the images into low resolution matrices, blurring the low resolution images, segmenting portions of the blurred low resolution matrices based on the detected edges, matching the digital images based on a cross-correlation match between the segmented portions, performing non-linear warping to further match Regions of Interest (ROI), and performing image subtraction between the matched digital images. The low resolution matrices are greater than 64.times.64 in size and are produced by averaging. Blurring of the low resolution matrices is performed via a Gaussian filter that removes fine structures in each image such as small vessels, bronchia, etc. The method may be performed by a computer system according to instructions stored on a computer readable medium.

摘要翻译： 一种方法和计算机化的自动初始图像匹配技术，用于通过图像减法增强时间上随后的放射照相图像之间的间隔变化的检测。 该方法包括数字化图像数字化，标准化数字图像中的浓度和对比度，校正数字图像中的横向倾斜，检测每个图像中相同特征的边缘，将图像转换成低分辨率矩阵，模糊低分辨率图像 基于检测到的边缘对模糊的低分辨率矩阵的部分进行分割，基于分段部分之间的互相关匹配匹配数字图像，执行非线性翘曲以进一步匹配感兴趣区域（ROI），并执行图像相减 在匹配的数字图像之间。 低分辨率矩阵大小大于64x64，并通过平均生成。 通过高斯滤波器执行低分辨率矩阵的模糊，其去除每个图像中的精细结构，例如小血管，支气管等。该方法可以由计算机系统根据存储在计算机可读介质上的指令来执行。

公开(公告)号：US5345513A

公开(公告)日：1994-09-06

申请号：US856993

申请日：1992-03-24

申请人： Siro Takeda ,  Fumihiro Namiki ,  Takahiro Haraki ,  Hideyuki Hirano ,  Kenji Ishiwata ,  Shigehiko Katsuragawa ,  Katsumi Abe

发明人： Siro Takeda ,  Fumihiro Namiki ,  Takahiro Haraki ,  Hideyuki Hirano ,  Kenji Ishiwata ,  Shigehiko Katsuragawa ,  Katsumi Abe

IPC分类号： A61B6/00 ,  G01T1/29 ,  G06T5/20 ,  H04N5/32 ,  H05G1/60 ,  G06K9/00

CPC分类号： G06T5/004 ,  G01T1/2014 ,  G06T5/007 ,  G06T5/30 ,  H04N5/32 ,  H05G1/60

摘要： The present invention relates to a method for processing an image corresponding to a radiographic pattern, the image being formed of a plurality of pixels each having a value corresponding to density. The method includes the steps of obtaining an image corresponding to a radiographic pattern so that values of pixels forming the image are stored in a memory; dividing the image into a plurality of image regions based on the values of the pixels forming the image, each image region including pixels each of which has a value falling within a predetermined range; and applying an image processing to the plurality of image regions of the image, processing conditions used for the plurality of image regions in the image processing differing from each other.

摘要翻译： 本发明涉及一种用于处理与放射线图像相对应的图像的方法，该图像由多个像素组成，每个像素具有与密度对应的值。 该方法包括以下步骤：获得与放射照相图案相对应的图像，使得形成图像的像素的值被存储在存储器中; 基于形成图像的像素的值将图像划分为多个图像区域，每个图像区域包括各自具有落在预定范围内的值的像素; 以及对图像的多个图像区域应用图像处理，在图像处理中用于多个图像区域的处理条件彼此不同。