公开(公告)号：US08898169B2

公开(公告)日：2014-11-25

申请号：US13292852

申请日：2011-11-09

申请人： Salih B. Gokturk ,  Wei Zhang ,  Emilio Rodriguez Antunez, III ,  Baris Sumengen

发明人： Salih B. Gokturk ,  Wei Zhang ,  Emilio Rodriguez Antunez, III ,  Baris Sumengen

IPC分类号： G06F17/30 ,  G06Q30/02

CPC分类号： G06F17/30873 ,  G06Q30/0278

摘要： Product data for a product is received by an attribute selection module. The product data includes product image data and product text data. This product data is used to generate a plurality of probability distributions for a category. The category includes a plurality of attributes, and the probability distribution includes a plurality of probabilities indicating the likelihoods that attributes of the category are applicable to the product. The plurality of probability distributions for the category are weighted and summed to generate a combined probability distribution for the category. An attribute label is determined by selecting an attribute from the category that is indicated to be most likely applicable to the product based on the combined probability distribution for the category. The attribute label is associated with the product. The attribute label enables other services to search for and retrieve the product based on the attribute.

摘要翻译： 产品的产品数据由属性选择模块接收。 产品数据包括产品图像数据和产品文本数据。 该产品数据用于为类别生成多个概率分布。 该类别包括多个属性，并且概率分布包括指示类别的属性适用于产品的可能性的多个概率。 对类别的多个概率分布进行加权和求和，以生成该类别的组合概率分布。 通过根据该类别的组合概率分布来选择指示为最可能适用于该产品的类别的属性来确定属性标签。 属性标签与产品相关联。 属性标签使其他服务可以根据属性搜索和检索产品。

公开(公告)号：US20120117072A1

公开(公告)日：2012-05-10

申请号：US13292852

申请日：2011-11-09

申请人： Salih B. Gokturk ,  Wei Zhang ,  Emilio Rodriguez Antunez, III ,  Baris Sumengen

发明人： Salih B. Gokturk ,  Wei Zhang ,  Emilio Rodriguez Antunez, III ,  Baris Sumengen

IPC分类号： G06F17/30

CPC分类号： G06F17/30873 ,  G06Q30/0278

摘要： Product data for a product is received by an attribute selection module. The product data includes product image data and product text data. This product data is used to generate a plurality of probability distributions for a category. The category includes a plurality of attributes, and the probability distribution includes a plurality of probabilities indicating the likelihoods that attributes of the category are applicable to the product. The plurality of probability distributions for the category are weighted and summed to generate a combined probability distribution for the category. An attribute label is determined by selecting an attribute from the category that is indicated to be most likely applicable to the product based on the combined probability distribution for the category. The attribute label is associated with the product. The attribute label enables other services to search for and retrieve the product based on the attribute.

摘要翻译： 产品的产品数据由属性选择模块接收。 产品数据包括产品图像数据和产品文本数据。 该产品数据用于为类别生成多个概率分布。 该类别包括多个属性，并且概率分布包括指示类别的属性适用于产品的可能性的多个概率。 对类别的多个概率分布进行加权和求和，以生成该类别的组合概率分布。 通过根据该类别的组合概率分布来选择指示为最可能适用于该产品的类别的属性来确定属性标签。 属性标签与产品相关联。 属性标签使其他服务可以根据属性搜索和检索产品。

公开(公告)号：US07346209B2

公开(公告)日：2008-03-18

申请号：US10676839

申请日：2003-09-30

申请人： Salih B. Gokturk ,  Carlo Tomasi ,  Acar Burak ,  Christopher F. Beaulieu ,  Sandy A. Napel ,  David S. Paik

发明人： Salih B. Gokturk ,  Carlo Tomasi ,  Acar Burak ,  Christopher F. Beaulieu ,  Sandy A. Napel ,  David S. Paik

IPC分类号： G06K9/62

CPC分类号： G06K9/4609 ,  G06K9/4647 ,  G06K2209/05 ,  G06T7/0012 ,  G06T2207/30028

摘要： A detection and classification method of a shape in a medical image is provided. It is based on generating a plurality of 2-D sections through a 3-D volume in the medical image. In general, there are two steps. The first step is feature estimation to generate shape signatures for candidate volumes containing candidate shapes. The feature estimation method computes descriptors of objects or of their images. The second general step involves classification of these shape signatures for diagnosis. A classifier contains, builds and/or trains a database of descriptors for previously seen shapes, and then maps descriptors of novel images to categories corresponding to previously seen shapes or classes of shapes.

摘要翻译： 提供了医疗图像中的形状的检测和分类方法。 它基于在医学图像中通过3-D体积产生多个2-D部分。 一般来说，有两个步骤。 第一步是为包含候选形状的候选卷生成形状签名的特征估计。 特征估计方法计算对象或其图像的描述符。 第二个一般步骤包括对这些形状特征进行分类以进行诊断。 分类器包含，构建和/或训练先前看到的形状的描述符数据库，然后将新颖图像的描述符映射到与以前看到的形状或类别形状对应的类别。

公开(公告)号：US07272251B2

公开(公告)日：2007-09-18

申请号：US10676721

申请日：2003-09-30

申请人： Burak Acar ,  Christopher F. Beaulieu ,  Salih B. Gokturk ,  Carlo Tomasi ,  David S. Paik ,  R. Brooke Jeffrey, Jr. ,  Sandy A. Napel

发明人： Burak Acar ,  Christopher F. Beaulieu ,  Salih B. Gokturk ,  Carlo Tomasi ,  David S. Paik ,  R. Brooke Jeffrey, Jr. ,  Sandy A. Napel

IPC分类号： G06K9/00

CPC分类号： G06T7/0012 ,  G06K9/00201 ,  G06T2207/30028 ,  G06T2207/30061

摘要： A method to detect and classify a structure of interest in a medical image is provided to enable high specificity without sacrificing the sensitivity of detection. The method is based on representing changes in three-dimensional image data with a vector field, characterizing the topology of this vector field and using the characterized topology of the vector field for classification of a structure of interest. The method could be used as a stand-alone method or as a post-processing method to enhance and classify outputs of a high-sensitivity low-specificity method to eliminate false positives.

摘要翻译： 提供了一种用于检测和分类医学图像中感兴趣的结构的方法，以实现高特异性而不牺牲检测的灵敏度。 该方法基于用矢量场表示三维图像数据的变化，表征该矢量场的拓扑，并且使用矢量场的表征拓扑来分类感兴趣的结构。 该方法可用作独立方法或作为后处理方法来增强和分类高灵敏度低特异性方法的输出以消除假阳性。