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29 条记录 (耗时 0.016 秒)

    Characterization of lung nodules
    12.
    发明申请
    Characterization of lung nodules 审中-公开

    公开(公告)号：US20090052763A1

    公开(公告)日：2009-02-26

    申请号：US12132365

    申请日：2008-06-03

    申请人： Mausumi Acharyya ,  Sumit Chakravarty ,  Jonathan Stoeckel

    发明人： Mausumi Acharyya ,  Sumit Chakravarty ,  Jonathan Stoeckel

    IPC分类号： G06K9/00

    CPC分类号： G06T7/0012 ,  G06T7/13 ,  G06T2207/10116 ,  G06T2207/20012 ,  G06T2207/30061 ,  G06T2207/30064 ,  G06T2207/30196

    摘要： A method of identifying nodules in radiological images, said method comprising: (a) obtaining a radiological image; (b) selecting a sub-image centered around a candidate location; (c) dividing the sub-image into a rectangular array of cells; (d) calculating absolute values of Intensity Differences id(k) according to a Fractional Brownian Motion (FBM) calculation equation: id ( k ) = [ ∑ x = 0 N - 1  ∑ y = 0 N - k - 1   I  ( x , y ) - I  ( x , y + k )  4  N  ( N - k ) + ∑ y = 0 N - 1  ∑ x = 0 N - k - 1   I  ( x , y ) - I  ( x + k , y )  4  N  ( N - k ) + ∑ x = 0 N - 1 - k  ∑ y = 0 N - k - 1   I  ( x , y ) - I  ( x + k , y + k )  4  ( N - k ) 2 + ∑ x = 0 N - 1 - k  ∑ y = 0 N - k - 1   I  ( x , N - y ) - I  ( x + k , N - ( y + k ) )  4  ( N - k ) 2 ] , for k=1 to s; (e) calculating a NFBM feature, f(k), for each id(k): f(k)=log(id(k))−log(id(1); (f) integrating f(k), over k=1 to s; (i) classifying the cells into intensity contrast classes, according to intensity contrast between each cell and its neighbors, and the integration result; (k) remapping each cell of the sub-image according to its contrast class, and (m) determining the shape of the region of high-contrast cells in the sub-image, wherein an annular shape identifies a nodule.

    Automatic CAD algorithm selection
    13.
    发明申请
    Automatic CAD algorithm selection 有权
    自动CAD算法选择

    公开(公告)号：US20070076934A1

    公开(公告)日：2007-04-05

    申请号：US11543416

    申请日：2006-10-05

    申请人： Arun Krishnan ,  Jonathan Stoeckel

    发明人： Arun Krishnan ,  Jonathan Stoeckel

    IPC分类号： G06K9/00 ,  G06F19/00

    CPC分类号： G06T7/0012 ,  G06F19/00 ,  G06F19/321 ,  G06Q50/24 ,  G06T2207/30004 ,  G16H50/20

    摘要： A computer system for automatic selection of a computer-aided detection (CAD) algorithm including a database storing image data, a browser for navigating the data and selecting image data, an application receiving image data selected by the browser, and a selector selecting a CAD algorithm for processing the image data according to at least one of fixed attributes of the image data and an indication of a subject of the image data.

    摘要翻译： 一种用于自动选择计算机辅助检测（CAD）算法的计算机系统，包括存储图像数据的数据库，用于导航数据和选择图像数据的浏览器，接收由浏览器选择的图像数据的应用程序，以及选择CAD 算法，用于根据图像数据的固定属性和图像数据的对象的指示中的至少一个来处理图像数据。

    Incorporating spatial knowledge for classification
    14.
    发明申请
    Incorporating spatial knowledge for classification 有权
    结合空间知识进行分类

    公开(公告)号：US20050058338A1

    公开(公告)日：2005-03-17

    申请号：US10915076

    申请日：2004-08-10

    申请人： Arun Krishnan ,  Glenn Fung ,  Jonathan Stoeckel

    发明人： Arun Krishnan ,  Glenn Fung ,  Jonathan Stoeckel

    IPC分类号： G06F19/00 ,  G06K9/00 ,  G06K9/62 ,  G06K9/68 ,  G06T7/00

    CPC分类号： G06T7/0012 ,  G06K9/6807 ,  G06T2207/10072 ,  G06T2207/20012 ,  G06T2207/30064

    摘要： We propose using different classifiers based on the spatial location of the object. The intuitive idea behind this approach is that several classifiers may learn local concepts better than a “universal” classifier that covers the whole feature space. The use of local classifiers ensures that the objects of a particular class have a higher degree of resemblance within that particular class. The use of local classifiers also results in memory, storage and performance improvements, especially when the classifier is kernel-based. As used herein, the term “kernel-based classifier” refers to a classifier where a mapping function (i.e., the kernel) has been used to map the original training data to a higher dimensional space where the classification task may be easier.

    摘要翻译： 我们建议基于对象的空间位置使用不同的分类器。 这种方法背后的直观思想是，几个分类器可以比涵盖整个特征空间的“通用”分类器更好地学习局部概念。 使用本地分类器确保特定类的对象在该特定类中具有更高程度的相似度。 使用本地分类器也会导致内存，存储和性能改进，特别是当分类器是基于内核的时候。 如本文所使用的，术语“基于内核的分类器”是指其中已经使用映射函数（即，内核）将原始训练数据映射到更高维度空间的分类器，其中分类任务可以更容易。

    Data for Use of Accessible Computer Assisted Detection
    16.
    发明申请
    Data for Use of Accessible Computer Assisted Detection 审中-公开
    使用无障碍计算机辅助检测的数据

    公开(公告)号：US20100138240A1

    公开(公告)日：2010-06-03

    申请号：US12623813

    申请日：2009-11-23

    申请人： David Leib ,  Jonathan Stoeckel

    发明人： David Leib ,  Jonathan Stoeckel

    IPC分类号： G06Q50/00 ,  G06K9/00

    CPC分类号： G06F19/321 ,  G06Q50/24

    摘要： Computer assisted detection (CAD) is made accessible to more medical offices. The CAD is provided as a service. Customers gain access to CAD service through a computer network but without the purchase of expensive software and/or hardware. The customers use software for extracting needed patient data to use the CAD service. The CAD service provider uses a server farm or third party server facilities, allowing growth without as substantial upfront costs. The CAD service provider collects patient data by providing the service. The aggregated patient data allows training of different or improved CAD algorithms. The service also identifies suspect data, such as associated with incorrect imaging settings, and provides help to the customers.

    摘要翻译： 计算机辅助检测（CAD）可以访问更多的医疗机构。 CAD作为服务提供。 客户可以通过计算机网络访问CAD服务，但无需购买昂贵的软件和/或硬件。 客户使用软件提取所需的患者数据以使用CAD服务。 CAD服务提供商使用服务器场或第三方服务器设施，允许增长，而不需要大量的前期成本。 CAD服务提供商通过提供服务收集患者数据。 汇总的患者数据允许对不同或改进的CAD算法进行训练。 该服务还会识别可疑数据，例如与不正确的映像设置相关联，并为客户提供帮助。

    COMPUTER-AIDED DETECTION WITH ENHANCED WORKFLOW
    17.
    发明申请
    COMPUTER-AIDED DETECTION WITH ENHANCED WORKFLOW 审中-公开
    计算机辅助检测与增强的工作流程

    公开(公告)号：US20100135562A1

    公开(公告)日：2010-06-03

    申请号：US12625499

    申请日：2009-11-24

    申请人： Michael Greenberg ,  Isaao Leichter ,  Jonathan STOECKEL

    发明人： Michael Greenberg ,  Isaao Leichter ,  Jonathan STOECKEL

    IPC分类号： G06K9/00 ,  G06K9/46

    CPC分类号： G06T7/0012 ,  G06F19/321 ,  G06T2200/24 ,  G06T2207/10072 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/30068

    摘要： Described herein is a technology for supporting an efficient workflow. In one implementation, a computer system receives at least one image of a subject and at least one corresponding image finding (302). The image finding identifies one or more regions-of-interest in a subject area of the image. The computer system generates enhanced annotations based on the image finding (306), overlays the enhanced annotations on the image (310) and displays (312) the resulting image to facilitate image assessment by a skilled user.

    摘要翻译： 这里描述了一种用于支持高效工作流的技术。 在一个实现中，计算机系统接收对象的至少一个图像和至少一个对应的图像发现（302）。 图像发现识别图像的主题区域中的一个或多个感兴趣区域。 计算机系统基于图像发现（306）生成增强的注释，覆盖图像上的增强注释（310）并显示（312）所得到的图像以便于熟练用户进行图像评估。

    Method and system for local visualization for tubular structures
    18.
    发明授权
    Method and system for local visualization for tubular structures 有权
    用于管状结构局部可视化的方法和系统

    公开(公告)号：US07684602B2

    公开(公告)日：2010-03-23

    申请号：US11103091

    申请日：2005-04-11

    申请人： Pascal Cathier ,  Jonathan Stoeckel

    发明人： Pascal Cathier ,  Jonathan Stoeckel

    IPC分类号： G06K9/00

    CPC分类号： G06T7/73 ,  G06T2207/30101

    摘要： A method of visualizing an object in an image includes presenting an image, selecting a point in an object of interest in said image, estimating a gradient of the image in a region about the selected point, calculating a structure tensor from the image gradient, analyzing said structure tensor to determine a main orientation of said object of interest, and presenting a visualization of said object of interest based on the main orientation of the object. Various techniques can be used to increase the robustness of the gradient estimation with respect to noise, and to enhance the visualization of the object-of-interest presented to a user.

    摘要翻译： 可视化图像中的对象的方法包括呈现图像，在所述图像中选择感兴趣对象中的点，估计关于所选点的区域中的图像的梯度，从图像梯度计算结构张量，分析 所述结构张量来确定所述感兴趣对象的主要取向，以及基于所述对象的主要取向呈现所述感兴趣对象的可视化。 可以使用各种技术来增加关于噪声的梯度估计的鲁棒性，并且增强呈现给用户的兴趣对象的可视化。

    System and method for body extraction in medical image volumes
    19.
    发明授权
    System and method for body extraction in medical image volumes 有权
    医学图像体内体内提取的系统和方法

    公开(公告)号：US07650025B2

    公开(公告)日：2010-01-19

    申请号：US11493308

    申请日：2006-07-26

    申请人： Grégoire Guétat ,  Jonathan Stoeckel ,  Matthias Wolf

    发明人： Grégoire Guétat ,  Jonathan Stoeckel ,  Matthias Wolf

    IPC分类号： G06K9/00

    CPC分类号： A61B6/5252 ,  G06T7/12 ,  G06T7/149 ,  G06T2207/10081 ,  G06T2207/30004

    摘要： A method for identifying non-body structures in digitized medical images including the steps of providing a digitized image comprising a plurality of intensities corresponding to a domain of points on an N-dimensional grid, wherein said image includes a representation of a body and of non-body structures separate from said body, initializing a surface in said image on a side of said non-body structures opposite from said body, defining a plurality of forces acting on said surface, and displacing said surface through said non-body structures using said forces until said body is encountered.

    摘要翻译： 一种用于识别数字化医学图像中的非身体结构的方法，包括以下步骤：提供包括与N维网格上的点的区域相对应的多个强度的数字化图像，其中所述图像包括身体和非身体结构的表示， 与所述身体分开的身体结构，在与所述身体相对的所述非身体结构的一侧初始化所述图像中的表面，限定作用在所述表面上的多个力，并且使用所述身体结构使所述表面移位通过所述非身体结构 直到遇到身体。