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    Automatic CAD algorithm selection
    1.
    发明授权
    Automatic CAD algorithm selection 有权
    自动CAD算法选择

    公开(公告)号：US08134571B2

    公开(公告)日：2012-03-13

    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： G06F15/00 ,  G06F7/00 ,  G09G5/00 ,  G06K9/00 ,  G06K9/40 ,  G06K9/36 ,  G06K9/60

    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。

    Shape index weighted voting for detection of objects
    2.
    发明授权
    Shape index weighted voting for detection of objects 有权
    形状指数加权投票用于检测物体

    公开(公告)号：US07529395B2

    公开(公告)日：2009-05-05

    申请号：US11067542

    申请日：2005-02-24

    申请人： Pascal Cathier ,  Xiangwei Zhang ,  Jonathan Stoeckel ,  Matthias Wolf

    发明人： Pascal Cathier ,  Xiangwei Zhang ,  Jonathan Stoeckel ,  Matthias Wolf

    IPC分类号： G06K9/00

    CPC分类号： G06T7/0012 ,  G06K9/4671 ,  G06T2207/30061 ,  Y10S128/922

    摘要： In one aspect of the present invention, a method for calculating a response value at a first voxel indicative of a global shape in an image is provided. The method includes the steps of (a) determining at least one local shape descriptor associated with each of the at least one local shape descriptor; (b) determining a spread function associated with the each of the at least one local shape descriptor; (c) determining second voxels around the first voxel; (d) calculating values for each the at least one local shape descriptor at each of the second voxels; (e) determining a contribution of each of the second voxels at the first voxel based on the spread functions; and (f) using a combination function to combine the contributions to determine the response value indicative of the global shape.

    摘要翻译： 在本发明的一个方面，提供了一种用于计算在图像中指示全局形状的第一体素处的响应值的方法。 该方法包括以下步骤：（a）确定与所述至少一个局部形状描述符中的每一个相关联的至少一个局部形状描述符; （b）确定与所述至少一个局部形状描述符中的每一个相关联的扩展函数; （c）确定第一体素周围的第二体素; （d）计算每个所述第二体素中的每个所述至少一个局部形状描述符的值; （e）基于扩展函数确定第一体素中的每个第二体素的贡献; 和（f）使用组合函数来组合贡献以确定指示全局形状的响应值。

    System and method for a contiguous support vector machine
    3.
    发明申请
    System and method for a contiguous support vector machine 审中-公开
    用于连续支持向量机的系统和方法

    公开(公告)号：US20060104519A1

    公开(公告)日：2006-05-18

    申请号：US11262041

    申请日：2005-10-28

    申请人： Jonathan Stoeckel ,  Glenn Fung

    发明人： Jonathan Stoeckel ,  Glenn Fung

    IPC分类号： G06K9/62 ,  G06K9/46 ,  G06K9/00

    CPC分类号： G06K9/6292 ,  G06T7/0012 ,  G06T7/33 ,  G06T2207/30016

    摘要： A method of classifying features in digitized images includes providing a plurality of feature points in an n-dimensional space, wherein said feature points have been extracted from a digitized medical image, formulating a support vector machine to classify said feature point into one of two sets, wherein each said feature classification vector is transformed by an adjacency matrix defined by those points that are nearest neighbors of said feature, and solving said support vector machine by a linear optimization algorithm to determine a classifying plane that separates the feature vectors into said two sets.

    摘要翻译： 对数字化图像中的特征进行分类的方法包括在n维空间中提供多个特征点，其中所述特征点已经从数字化医学图像中提取出来，制定支持向量机以将所述特征点分类成两组中的一组 其中每个所述特征分类矢量被由所述特征的最近邻的那些点定义的相邻矩阵变换，并且通过线性优化算法求解所述支持向量机，以确定将特征向量分成所述两组的分类平面 。

    Incorporating spatial knowledge for classification
    5.
    发明授权
    Incorporating spatial knowledge for classification 有权
    结合空间知识进行分类

    公开(公告)号：US07634120B2

    公开(公告)日：2009-12-15

    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： G06K9/00

    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。

    System and Method for Automatic Detection of Internal Structures in Medical Images
    6.
    发明申请
    System and Method for Automatic Detection of Internal Structures in Medical Images 有权
    医学图像内部结构自动检测系统与方法

    公开(公告)号：US20070248254A1

    公开(公告)日：2007-10-25

    申请号：US11694050

    申请日：2007-03-30

    申请人： Dinesh Mysore Siddu ,  Mausumi Acharyya ,  Jonathan Stoeckel

    发明人： Dinesh Mysore Siddu ,  Mausumi Acharyya ,  Jonathan Stoeckel

    IPC分类号： G06K9/00

    CPC分类号： G06K9/4638 ,  G06K2209/05 ,  G06T7/11 ,  G06T2207/10072 ,  G06T2207/20012 ,  G06T2207/30032 ,  G06T2207/30048 ,  G06T2207/30064

    摘要： A medical imaging system is used to recognize an internal structure from a three-dimensional image. The image includes image sub-volumes. An image sub-volume is selected using a non-linear search pattern. The selected image sub-volume is analyzed for the presence of the internal structure. The steps of selecting an image sub-volume using the non-linear search pattern and analyzing the selected sub-volume for the presence of the internal structure are repeated until the internal structure is found in an image sub-volume. Bounds of the internal structure are identified based on the location of the image sub-volume within which the internal structure is found.

    摘要翻译： 医学成像系统用于从三维图像识别内部结构。 图像包括图像子卷。 使用非线性搜索模式选择图像子卷。 分析所选图像子体积是否存在内部结构。 重复使用非线性搜索模式选择图像子体积并分析所选择的子体积以存在内部结构的步骤，直到在图像子体积中找到内部结构。 内部结构的界限是根据找到内部结构的图像子体积的位置进行识别的。

    System and method for automatic detection of internal structures in medical images
    9.
    发明授权
    System and method for automatic detection of internal structures in medical images 有权
    医学图像内部结构的自动检测系统及方法

    公开(公告)号：US08090178B2

    公开(公告)日：2012-01-03

    申请号：US11694050

    申请日：2007-03-30

    申请人： Dinesh Mysore Siddu ,  Mausumi Acharyya ,  Jonathan Stoeckel

    发明人： Dinesh Mysore Siddu ,  Mausumi Acharyya ,  Jonathan Stoeckel

    IPC分类号： G06K9/00

    CPC分类号： G06K9/4638 ,  G06K2209/05 ,  G06T7/11 ,  G06T2207/10072 ,  G06T2207/20012 ,  G06T2207/30032 ,  G06T2207/30048 ,  G06T2207/30064

    摘要： A medical imaging system is used to recognize an internal structure from a three-dimensional image. The image includes image sub-volumes. An image sub-volume is selected using a non-linear search pattern. The selected image sub-volume is analyzed for the presence of the internal structure. The steps of selecting an image sub-volume using the non-linear search pattern and analyzing the selected sub-volume for the presence of the internal structure are repeated until the internal structure is found in an image sub-volume. Bounds of the internal structure are identified based on the location of the image sub-volume within which the internal structure is found.

    摘要翻译： 医学成像系统用于从三维图像识别内部结构。 图像包括图像子卷。 使用非线性搜索模式选择图像子卷。 分析所选图像子体积是否存在内部结构。 重复使用非线性搜索模式选择图像子体积并分析所选择的子体积以存在内部结构的步骤，直到在图像子体积中找到内部结构。 内部结构的界限是根据找到内部结构的图像子体积的位置进行识别的。

    System and method for quality assurance for distributed computer aided diagnosis solutions
    10.
    发明授权
    System and method for quality assurance for distributed computer aided diagnosis solutions 有权
    分布式计算机辅助诊断解决方案的质量保证体系和方法

    公开(公告)号：US07688995B2

    公开(公告)日：2010-03-30

    申请号：US11493309

    申请日：2006-07-26

    申请人： Jonathan Stoeckel

    发明人： Jonathan Stoeckel

    IPC分类号： G06K9/00

    CPC分类号： G06T7/0012 ,  G06F19/00 ,  G06F19/321

    摘要： A method for associating a computer aided detection/diagnosis result with an image including the steps of providing one or more digitized images, each image comprising a plurality of intensities corresponding to a domain of points on an N-dimensional grid, performing a computer-aided detection/diagnosis of intensity data of a first image, calculating a hash signature of intensity of a second input image, storing said computer-aided diagnosis results, and storing said hash signature, wherein said hash signature verifies said second image when said second image is displayed with said computer-aided diagnosis result.

    摘要翻译： 一种用于将计算机辅助检测/诊断结果与包括提供一个或多个数字化图像的步骤相关联的图像的方法，每个图像包括对应于N维网格上的点的域的多个强度，执行计算机辅助 检测/诊断第一图像的强度数据，计算第二输入图像的强度的散列签名，存储所述计算机辅助诊断结果，以及存储所述散列签名，其中当所述第二图像为 用计算机辅助诊断结果显示。