利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

128 条记录 (耗时 0.061 秒)

    Method and system for locating opaque regions in chest X-ray radiographs
    31.
    发明授权
    Method and system for locating opaque regions in chest X-ray radiographs 失效
    用于定位胸部X线片中不透明区域的方法和系统

    公开(公告)号：US08103077B2

    公开(公告)日：2012-01-24

    申请号：US11951372

    申请日：2007-12-06

    申请人： Lin Hong ,  Ying Li ,  Hong Shen

    发明人： Lin Hong ,  Ying Li ,  Hong Shen

    IPC分类号： G06K9/00

    CPC分类号： G06T7/12

    摘要： A method and system for locating an opaque region, such as a heart region in a chest X-ray radiograph is disclosed. In order to segment a heart region in a chest X-ray radiograph, a heart region boundary is generated based on lung boundaries in the chest X-ray radiograph and an average heart region model. A location of the lower boundary of the heart region in the chest X-ray radiograph is then determined. Left and right portions of the heart region boundary are independently registered to corresponding portions of the lung boundaries, and upper and lower portions of the heart region boundary are adjusted based on the left and right portions in order to form a smooth contour.

    摘要翻译： 公开了一种用于定位不透明区域的方法和系统，例如胸部X射线照片中的心脏区域。 为了在胸部X射线照片中分割心脏区域，基于胸部X射线照片和平均心脏区域模型中的肺边界产生心脏区域边界。 然后确定胸部X线片中的心脏区域的下边界的位置。 心脏区域边界的左右部分独立地登记到肺边界的对应部分，并且基于左右部分来调整心脏区域边界的上部和下部以形成平滑轮廓。

    Machine Learning For Tissue Labeling Segmentation
    33.
    发明申请
    Machine Learning For Tissue Labeling Segmentation 有权
    机器学习用于组织标签分割

    公开(公告)号：US20090116737A1

    公开(公告)日：2009-05-07

    申请号：US12261383

    申请日：2008-10-30

    申请人： Atilla Peter Kiraly ,  Christopher V. Alvino ,  Hong Shen

    发明人： Atilla Peter Kiraly ,  Christopher V. Alvino ,  Hong Shen

    IPC分类号： G06K9/62

    CPC分类号： G06K9/6269 ,  G06K9/0014 ,  G06T7/11 ,  G06T7/143 ,  G06T2207/30024 ,  G06T2207/30096

    摘要： A method for directed machine learning includes receiving features including intensity data and location data of an image, condensing the intensity data and the location data into a feature vector, processing the feature vector by a plurality of classifiers, each classifier trained for a respective trained class among a plurality of classes, outputting, from each classifier, a probability of the feature vector belong to the respective trained class, and assigning the feature vector a label according to the probabilities of the classifiers, wherein the assignment produces a segmentation of the image.

    摘要翻译： 用于定向机器学习的方法包括接收包括强度数据和图像的位置数据的特征，将强度数据和位置数据聚合成特征向量，通过多个分类器处理特征向量，每个分类器针对相应的训练类进行训练 在多个类中，从每个分类器输出特征向量属于相应训练类的概率，并根据分类器的概率向特征向量分配标签，其中分配产生图像的分割。

    Systems and Methods For Segmenting Object Of Interest From Medical Image
    35.
    发明申请
    Systems and Methods For Segmenting Object Of Interest From Medical Image 有权
    从医学图像分割兴趣对象的系统和方法

    公开(公告)号：US20070081710A1

    公开(公告)日：2007-04-12

    申请号：US11537673

    申请日：2006-10-02

    申请人： Lin Hong ,  Hong Shen

    发明人： Lin Hong ,  Hong Shen

    IPC分类号： G06K9/00

    CPC分类号： G06K9/38 ,  G06K2209/053 ,  G06T7/11 ,  G06T7/143 ,  G06T7/194 ,  G06T2207/10081 ,  G06T2207/20104 ,  G06T2207/30056 ,  G06T2207/30096

    摘要： A system for segmenting a target organ tumor from an image includes a background model builder, a foreground model builder and a tumor region locator. The background model builder uses an intensity distribution estimate of voxels in an organ region in an image to build a background model. The foreground model builder uses an intensity distribution estimate of voxels in a target organ tumor to build a first foreground model. The tumor region locator uses the background model and the first foreground model to segment the target organ tumor to obtain a first segmentation result.

    摘要翻译： 用于从图像分割靶器官肿瘤的系统包括背景模型构建器，前景模型构建器和肿瘤区域定位器。 背景模型构建器使用图像中器官区域中的体素的强度分布估计来构建背景模型。 前景模型构建器使用目标器官肿瘤中的体素的强度分布估计来构建第一前景模型。 肿瘤区域定位器使用背景模型和第一前景模型来分割目标器官肿瘤以获得第一分割结果。

    System and method for detecting ground glass nodules in medical images
    36.
    发明申请
    System and method for detecting ground glass nodules in medical images 有权

    公开(公告)号：US20060153451A1

    公开(公告)日：2006-07-13

    申请号：US11324503

    申请日：2006-01-03

    申请人： Lin Hong ,  Kumao Lu ,  Hong Shen

    发明人： Lin Hong ,  Kumao Lu ,  Hong Shen

    IPC分类号： G06K9/34

    CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/143 ,  G06T2207/10081 ,  G06T2207/30064

    摘要： Detecting ground glass nodules in medical images includes calculating a probability distribution function of background lung tissue in a chest image, estimating a variation range of the background tissue probability distribution function, estimating a probability distribution function of an image point belonging to a ground glass nodule from the variation range of the background tissue probability distribution function by using a function corresponding to the variation range of the background tissue probability distribution function, and calculating a log likelihood function of the image from the background tissue probability distribution function and the estimated ground glass nodule probability distribution function, wherein the log likelihood function represents the confidence that a point in the image is not part of a ground glass nodule. The log likelihood function value for each point is compared to a confidence value of the background tissue, to determine if the point is a candidate ground glass nodule location.

    Method for local surface smoothing with application to chest wall nodule segmentation in lung CT data
    39.
    发明申请
    Method for local surface smoothing with application to chest wall nodule segmentation in lung CT data 失效
    方法用于局部表面平滑应用于胸壁结节分段的肺CT数据

    公开(公告)号：US20050001832A1

    公开(公告)日：2005-01-06

    申请号：US10870304

    申请日：2004-06-17

    申请人： Hong Shen ,  Bernhard Gobel ,  Benjamin Odry

    发明人： Hong Shen ,  Bernhard Gobel ,  Benjamin Odry

    IPC分类号： G06T5/00 ,  G06T11/00 ,  G06T15/00 ,  G06T17/40

    CPC分类号： G06T5/002 ,  G06T2200/04 ,  G06T2207/30032 ,  G06T2207/30064

    摘要： We present an algorithm for local surface smoothing in a defined Volume of Interest (“VOI”) cropped from three-dimensional (“3D”) volume data, such as lung computer tomography (“CT”) data. Because the VOI is generally a smooth and piecewise linear surface, the inclusion of one or more bumps may suggest an abnormality. In lung CT data, for example, such bumps can be nodules that are grown from the chest wall. The nodules may represent a possibility of lung cancer. Through surface smoothing, potential pathologies are separated from the surrounding anatomical structures. For example, nodules may be segmented from the chest wall. The separated pathologies can be analyzed as diagnostic evidence.

    摘要翻译： 我们提出了一个定义的兴趣感兴趣体系（“VOI”）中的局部表面平滑算法，从三维（“3D”）体数据裁剪，如肺部计算机断层扫描（“CT”）数据。 因为VOI通常是平滑和分段的线性表面，所以包含一个或多个凸块可能表示异常。 在肺CT数据中，例如，这种颠簸可以是从胸壁生长的结节。 结节可能代表肺癌的可能性。 通过表面平滑，潜在的病理与周围的解剖结构分离。 例如，可以从胸壁分割结节。 分离的病理学可以作为诊断证据进行分析。