公开(公告)号：US08121367B2

公开(公告)日：2012-02-21

申请号：US12229663

申请日：2008-08-26

申请人： Richard Socher ,  Adrian Barbu ,  Bogdan Georgescu ,  Wei Zhang ,  Peter Durlak ,  Stefan Böhm ,  Dorin Comaniciu

发明人： Richard Socher ,  Adrian Barbu ,  Bogdan Georgescu ,  Wei Zhang ,  Peter Durlak ,  Stefan Böhm ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/6256 ,  G06K2009/00932 ,  G06T7/12 ,  G06T7/162 ,  G06T2207/10121 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method and system for vessel segmentation in fluoroscopic images is disclosed. Hierarchical learning-based detection is used to perform the vessel segmentation. A boundary classifier is trained and used to detect boundary pixels of a vessel in a fluoroscopic image. A cross-segment classifier is trained and used to detect cross-segments connecting the boundary pixels. A quadrilateral classifier is trained and used to detect quadrilaterals connecting the cross segments. Dynamic programming is then used to combine the quadrilaterals to generate a tubular structure representing the vessel.

摘要翻译： 公开了一种用于荧光镜图像中血管分割的方法和系统。 使用基于层次学习的检测来执行血管分割。 边界分类器被训练并用于检测透视图像中血管的边界像素。 训练跨段分类器，用于检测连接边界像素的交叉段。 四边形分类器被训练并用于检测连接横截面的四边形。 然后使用动态规划来组合四边形以产生代表该容器的管状结构。

公开(公告)号：US08086006B2

公开(公告)日：2011-12-27

申请号：US12231634

申请日：2008-09-04

申请人： Wei Zhang ,  Adrian Barbu ,  Simone Prummer ,  Martin Ostermeier ,  Dorin Comaniciu

发明人： Wei Zhang ,  Adrian Barbu ,  Simone Prummer ,  Martin Ostermeier ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06T7/60 ,  G06T7/11 ,  G06T7/174 ,  G06T7/246 ,  G06T2207/10121 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method and system for evaluating image segmentation is disclosed. In order to quantitatively evaluate an image segmentation technique, synthetic image data is generated and the synthetic image data is segmented to extract an object using the segmentation technique. This segmentation results in a foreground containing the extracted object and a background. The visibility of the extracted object is quantitatively measured based on the intensity distributions of the segmented foreground and background. The visibility is quantitatively measured by calculating the Jeffries-Matusita distance between the foreground and background intensity distributions. This method can be used to evaluate segmentation of vessels in fluoroscopic image sequences by coronary digital subtraction angiography (DSA).

摘要翻译： 公开了一种用于评估图像分割的方法和系统。 为了定量评估图像分割技术，生成合成图像数据，并且使用分割技术对合成图像数据进行分割以提取对象。 该分割导致包含提取的对象和背景的前景。 基于分割的前景和背景的强度分布，定量地测量提取的对象的可视性。 通过计算前景和背景强度分布之间的Jeffries-Matusita距离来定量测量可见度。 该方法可用于通过冠状动脉数字减影血管造影（DSA）评估荧光镜图像序列中血管的分割。

公开(公告)号：US08050482B2

公开(公告)日：2011-11-01

申请号：US11860588

申请日：2007-09-25

申请人： Adrian Barbu ,  Vassilis Athitsos ,  Bogdan Georgescu ,  Peter Durlak ,  Dorin Comaniciu

发明人： Adrian Barbu ,  Vassilis Athitsos ,  Bogdan Georgescu ,  Peter Durlak ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： A61B6/12 ,  G06T5/002 ,  G06T5/003 ,  G06T5/20 ,  G06T2207/10121 ,  G06T2207/30021 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method for online optimization of guidewire visibility in fluoroscopic images includes providing an digitized image acquired from a fluoroscopic imaging system, the image comprising an array of intensities corresponding to a 2-dimensional grid of pixels, detecting a guidewire in the fluoroscopic image, enhancing the visibility of the guidewire in the fluoroscopic image, calculating a visibility measure of the guidewire in the fluoroscopic image, and readjusting acquisition parameters of the fluoroscopic imaging system wherein the guidewire visibility is improved.

摘要翻译： 一种用于在荧光镜图像中在线优化导丝可视性的方法包括提供从荧光成像系统获取的数字化图像，所述图像包括对应于像素的二维网格的强度阵列，检测透视图像中的导丝，增强 在透视图像中的导丝的可视性，计算透视图像中的导丝的可见度测量，以及重新调整荧光镜成像系统的获取参数，其中导丝可视性得到改善。

公开(公告)号：US08009900B2

公开(公告)日：2011-08-30

申请号：US11856208

申请日：2007-09-17

申请人： Adrian Barbu ,  Yefeng Zheng ,  Jing Yang ,  Bogdan Georgescu ,  Dorin Comaniciu

发明人： Adrian Barbu ,  Yefeng Zheng ,  Jing Yang ,  Bogdan Georgescu ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06K9/6292

摘要： A system and method for detecting an object in a high dimensional image space is disclosed. A three dimensional image of an object is received. A first classifier is trained in the marginal space of the object center location which generates a predetermined number of candidate object center locations. A second classifier is trained to identify potential object center locations and orientations from the predetermined number of candidate object center locations and maintaining a subset of the candidate object center locations. A third classifier is trained to identify potential locations, orientations and scale of the object center from the subset of the candidate object center locations. A single candidate object pose for the object is identified.

摘要翻译： 公开了一种用于检测高维图像空间中的对象的系统和方法。 接收对象的三维图像。 在对象中心位置的边缘空间中训练第一分类器，其生成预定数量的候选对象中心位置。 训练第二分类器以从预定数量的候选对象中心位置识别潜在对象中心位置和方向，并维护候选对象中心位置的子集。 训练第三分类器以从候选对象中心位置的子集中识别对象中心的潜在位置，方向和尺度。 识别对象的单个候选对象姿势。

公开(公告)号：US07831074B2

公开(公告)日：2010-11-09

申请号：US11539989

申请日：2006-10-10

申请人： Shaohua Kevin Zhou ,  Jie Shao ,  Jonathan Dowdall ,  Adrian Barbu ,  Bogdan Georgescu ,  Dorin Comaniciu

发明人： Shaohua Kevin Zhou ,  Jie Shao ,  Jonathan Dowdall ,  Adrian Barbu ,  Bogdan Georgescu ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  G06K9/62 ,  H04N5/225

CPC分类号： G06K9/6257 ,  G06K2209/05 ,  G06T7/33 ,  G06T2207/10124 ,  G06T2207/10132 ,  G06T2207/30048

摘要： The present invention is directed to a method for populating a database with a set of images of an anatomical structure. The database is used to perform appearance matching in image pairs of the anatomical structure. A set of image pairs of anatomical structures is received, where each image pair is annotated with a plurality of location-sensitive regions that identify a particular aspect of the anatomical structure. Weak learners are iteratively selected and an image patch is identified. A boosting process is used to identify a strong classifier based on responses to the weak learners applied to the identified image patch for each image pair. The responses comprise a feature response and a location response associated with the image patch. Positive and negative image pairs are generated. The positive and negative image pairs are used to learn a similarity function. The learned similarity function and iteratively selected weak learners are stored in the database.

摘要翻译： 本发明涉及一种用解剖结构的一组图像填充数据库的方法。 该数据库用于在解剖结构的图像对中执行外观匹配。 接收一组解剖结构的图像对，其中每个图像对用多个识别解剖结构的特定方面的位置敏感区域注释。 迭代选择弱学习者，并识别图像补丁。 基于对应用于每个图像对的所识别的图像补丁的弱学习者的响应，使用增强过程来识别强分类器。 响应包括与图像块相关联的特征响应和位置响应。 产生正负图像对。 正负图像对用于学习相似度函数。 学习的相似度函数和迭代选择的弱学习者存储在数据库中。

公开(公告)号：US20070237370A1

公开(公告)日：2007-10-11

申请号：US11539989

申请日：2006-10-10

申请人： Shaohua Zhou ,  Jie Shao ,  Jonathan Dowdall ,  Adrian Barbu ,  Bogdan Georgescu ,  Dorin Comaniciu

发明人： Shaohua Zhou ,  Jie Shao ,  Jonathan Dowdall ,  Adrian Barbu ,  Bogdan Georgescu ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/6257 ,  G06K2209/05 ,  G06T7/33 ,  G06T2207/10124 ,  G06T2207/10132 ,  G06T2207/30048

摘要： The present invention is directed to a method for populating a database with a set of images of an anatomical structure. The database is used to perform appearance matching in image pairs of the anatomical structure. A set of image pairs of anatomical structures is received, where each image pair is annotated with a plurality of location-sensitive regions that identify a particular aspect of the anatomical structure. Weak learners are iteratively selected and an image patch is identified. A boosting process is used to identify a strong classifier based on responses to the weak learners applied to the identified image patch for each image pair. The responses comprise a feature response and a location response associated with the image patch. Positive and negative image pairs are generated. The positive and negative image pairs are used to learn a similarity function. The learned similarity function and iteratively selected weak learners are stored in the database.

摘要翻译： 本发明涉及一种用解剖结构的一组图像填充数据库的方法。 该数据库用于在解剖结构的图像对中执行外观匹配。 接收一组解剖结构的图像对，其中每个图像对用多个识别解剖结构的特定方面的位置敏感区域注释。 迭代选择弱学习者，并识别图像补丁。 基于对应用于每个图像对的所识别的图像补丁的弱学习者的响应，使用增强过程来识别强分类器。 响应包括与图像块相关联的特征响应和位置响应。 产生正负图像对。 正负图像对用于学习相似度函数。 学习的相似度函数和迭代选择的弱学习者存储在数据库中。