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    Computerized detection of lung nodules using energy-subtracted soft-tissue and standard chest images
    2.
    发明授权
    Computerized detection of lung nodules using energy-subtracted soft-tissue and standard chest images 失效
    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）。

    Computer-aided method for automated image feature analysis and diagnosis of medical images
    3.
    发明授权
    Computer-aided method for automated image feature analysis and diagnosis of medical images 失效
    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 。

    Adaptive segmentation of anatomic regions in medical images with fuzzy clustering
    5.
    发明申请
    Adaptive segmentation of anatomic regions in medical images with fuzzy clustering 审中-公开
    医学图像中解剖区域的自适应分割与模糊聚类

    公开(公告)号：US20050033139A1

    公开(公告)日：2005-02-10

    申请号：US10606120

    申请日：2003-06-26

    申请人： Ruiping Li ,  Xin-Wei Xu ,  Jyh-Shyan Lin ,  Fleming Lure ,  H.-Y Yeh

    发明人： Ruiping Li ,  Xin-Wei Xu ,  Jyh-Shyan Lin ,  Fleming Lure ,  H.-Y Yeh

    IPC分类号： A61B5/05 ,  G06T5/00 ,  G06T7/00

    CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/143 ,  G06T7/73 ,  G06T2207/10116 ,  G06T2207/20132 ,  G06T2207/30064

    摘要： A method for identifying the orientation of an interesting object in a digital medical image comprises steps of creating a rectangular interesting image mask that covers the interesting object, based on the original digital medical image; generating a rough image based on the interesting image mask, the rough image coarsely describing the interesting object; and identifying the orientation of the interesting object based on the rough image. A method for segmenting interesting objects in digital medical images may also comprise steps of creating a rectangular interesting image mask that covers said interesting object, based on an original digital medical image; generating a rough image based on the interesting image mask, the rough image coarsely describing the interesting object; and performing a post-process on the rough image.

    摘要翻译： 一种用于识别数字医学图像中感兴趣对象的取向的方法包括基于原始数字医学图像创建覆盖有趣对象的矩形有趣图像掩模的步骤; 基于有趣的图像掩模生成粗略图像，粗略图像粗略地描述感兴趣的对象; 并基于粗略图像识别感兴趣对象的方位。 用于分割数字医学图像中的感兴趣对象的方法还可以包括基于原始数字医学图像创建覆盖所述感兴趣对象的矩形有趣图像掩模的步骤; 基于有趣的图像掩模生成粗略图像，粗略图像粗略地描述感兴趣的对象; 并对粗略图像执行后处理。

    Fuzzy logic based classification (FLBC) method for automated identification of nodules in radiological images
    6.
    发明授权
    Fuzzy logic based classification (FLBC) method for automated identification of nodules in radiological images 失效
    基于模糊逻辑的分类（FLBC）方法，用于自动识别放射图像中的结节

    公开(公告)号：US06654728B1

    公开(公告)日：2003-11-25

    申请号：US09625418

    申请日：2000-07-25

    申请人： Ruiping Li ,  Hwa-Young Michael Yeh ,  Yuan-Ming Fleming Lure ,  Xin-Wei Xu ,  Jyh-Shyan Lin

    发明人： Ruiping Li ,  Hwa-Young Michael Yeh ,  Yuan-Ming Fleming Lure ,  Xin-Wei Xu ,  Jyh-Shyan Lin

    IPC分类号： G06F1518

    CPC分类号： G06T7/0012 ,  G06F19/321 ,  G06T2207/30061 ,  G16H50/20

    摘要： A fuzzy logic based classification (FLBC) method for the automated discrimination of objects and the automated identification of nodules based on their features, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule (or, object) features are first normalized and then automatically selected. Based on the selected features, suspect nodules (or, objects) are pre-grouped and then subjected to the corresponding trained linear classifier to remove those false positive nodules or abnormal objects that are linearly separable. Finally, the remaining suspect nodules or objects are further subjected to a trained fuzzy classifier for removing those false positive nodules or abnormal objects that are not linearly separable.

    摘要翻译： 一种用于自动识别物体的模糊逻辑分类（FLBC）方法和基于其特征的结节的自动识别，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，其中结节（ 或对象）特征首先被归一化，然后自动选择。 基于选定的特征，可疑结节（或对象）被预先分组，然后经受相应训练的线性分类器以去除可线性分离的假阳性结节或异常物体。 最后，剩余的嫌疑结节或物体进一步经受训练的模糊分类器，以去除不可线性分离的假阳性结节或异常物体。

    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
    8.
    发明授权
    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
    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。

    Input/output interface for computer aided diagnosis (CAD) system
    9.
    发明授权
    Input/output interface for computer aided diagnosis (CAD) system 失效
    计算机辅助诊断（CAD）系统的输入/输出接口

    公开(公告)号：US06873717B2

    公开(公告)日：2005-03-29

    申请号：US10414292

    申请日：2003-04-16

    申请人： Fleming Y. Lure ,  Rongfeng Zhang ,  Jyh-Shyan Lin ,  Xin-wei Xu ,  H. Michael Yeh

    发明人： Fleming Y. Lure ,  Rongfeng Zhang ,  Jyh-Shyan Lin ,  Xin-wei Xu ,  H. Michael Yeh

    IPC分类号： G06F19/00 ,  G06K9/00

    CPC分类号： G16H40/63 ,  G06F19/00 ,  G06F19/321 ,  G16H15/00

    摘要： A user interface for facilitating a computer-aided diagnosis (CAD) includes an image input device that receives and scans the multiple images, and a film feeder that transports films from a film developer to the scanner. A multi-image identification input device uses bar code reading, numeric keypad, keyboard, network, and/or mouse to enter multiple image identification prior to image scanning. A display includes an image identification area, case processing status area, and a CAD detection and user modification area. The user can edit CAD results—accept, remove—and can add new diagnosis results to create a new composite diagnosis result.

    摘要翻译： 用于促进计算机辅助诊断（CAD）的用户界面包括接收和扫描多个图像的图像输入装置，以及将胶片从胶片显影剂传送到扫描仪的胶片馈送器。 多图像识别输入装置使用条形码读取，数字键盘，键盘，网络和/或鼠标在图像扫描之前进入多个图像识别。 显示器包括图像识别区域，案例处理状态区域以及CAD检测和用户修改区域。 用户可以编辑CAD结果 - 接受，删除 - 并且可以添加新的诊断结果以创建新的复合诊断结果。