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1. 发明授权

US07471815B2 Candidate generation for lung nodule detection 有权
标题翻译：肺结节检测候选代
公开(公告)号：US07471815B2
公开(公告)日：2008-12-30
申请号：US11170421
申请日：2005-06-29
申请人： Lin Hong , Yonggang Shi , Hong Shen , Shuping Qing
发明人： Lin Hong , Yonggang Shi , Hong Shen , Shuping Qing
IPC分类号： G06K9/00 , A61B6/00 , G01N23/00 , G21K1/12 , H05G1/60
CPC分类号： G06T7/0012 , G06T2207/30061
摘要： A computer-implemented method for candidate generation in three-dimensional volumetric data comprises forming a binary volumetric image of the three-dimensional volumetric data including labeled foreground voxels, estimating a plurality of shape features of the labeled foreground voxels in the binary volumetric data including, identifying peak voxels and high curvature voxels from the foreground voxels in the binary volumetric image, accumulating a plurality of confidence values for boundary and each peak voxel, and detecting confidence peaks from the plurality of confidence values, wherein the confidence peaks are determined to be the candidate points, and refining the candidate points given detected confidence peaks, wherein refined candidate points are determined to be candidates.
摘要翻译：用于在三维体积数据中候选生成的计算机实现的方法包括形成包括标记的前景体素的三维体积数据的二进制体积图像，估计二进制体积数据中标记的前景体素的多个形状特征，从所述二维体积图像中的前景体素识别峰值体素和高曲率体素，为边界和每个峰体素累积多个置信度值，以及从所述多个置信度值中检测置信峰值，其中所述置信峰值被确定为候选点，并且提取给定检测到的置信峰的候选点，其中精确的候选点被确定为候选。

2. 发明申请

US20060044310A1 Candidate generation for lung nodule detection 有权
标题翻译：肺结节检测候选代
公开(公告)号：US20060044310A1
公开(公告)日：2006-03-02
申请号：US11170421
申请日：2005-06-29
申请人： Lin Hong , Yonggang Shi , Hong Shen , Shuping Qing
发明人： Lin Hong , Yonggang Shi , Hong Shen , Shuping Qing
IPC分类号： G06T17/00
CPC分类号： G06T7/0012 , G06T2207/30061
摘要： A computer-implemented method for candidate generation in three-dimensional volumetric data comprises forming a binary volumetric image of the three-dimensional volumetric data including labeled foreground voxels, estimating a plurality of shape features of the labeled foreground voxels in the binary volumetric data including, identifying peak voxels and high curvature voxels from the foreground voxels in the binary volumetric image, accumulating a plurality of confidence values for boundary and each peak voxel, and detecting confidence peaks from the plurality of confidence values, wherein the confidence peaks are determined to be the candidate points, and refining the candidate points given detected confidence peaks, wherein refined candidate points are determined to be candidates.
摘要翻译：用于在三维体积数据中候选生成的计算机实现的方法包括形成包括标记的前景体素的三维体积数据的二进制体积图像，估计二进制体积数据中标记的前景体素的多个形状特征，从所述二维体积图像中的前景体素识别峰值体素和高曲率体素，为边界和每个峰体素累积多个置信度值，以及从所述多个置信度值中检测置信峰值，其中所述置信峰值被确定为候选点，并且提取给定检测到的置信峰的候选点，其中精确的候选点被确定为候选。

3. 发明授权

US08073214B2 Computer aided lesion assessment in dynamic contrast enhanced breast MRI images 有权
标题翻译：计算机辅助病变评估动态对比增强胸部MRI图像
公开(公告)号：US08073214B2
公开(公告)日：2011-12-06
申请号：US12117155
申请日：2008-05-08
申请人： Lin Hong , Hong Shen , Shuping Qing
发明人： Lin Hong , Hong Shen , Shuping Qing
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T2207/10096 , G06T2207/30096
摘要： A method for performing computer-assisted lesion assessment includes acquiring contrast enhanced MRI data. A region of interest is identified. Kinetic properties, morphological properties and pattern of enhancement are determined for the identified region of interest. A score is calculated for each of the kinetic properties, morphological properties, pattern of enhancement. A cumulative score is calculated for the identified region of interest. The scores are provided to a medical practitioner to assist in diagnosing the identified region of interest.
摘要翻译：用于执行计算机辅助病变评估的方法包括获取对比度增强的MRI数据。确定感兴趣的区域。确定感兴趣区域的动力学性质，形态特征和增强模式。计算每个动力学性质，形态特征，增强模式的得分。计算所识别的感兴趣区域的累积分数。分数被提供给医生以帮助诊断所识别的感兴趣的区域。

4. 发明授权

US07602965B2 Object detection using cross-section analysis 失效
标题翻译：使用横截面分析的对象检测
公开(公告)号：US07602965B2
公开(公告)日：2009-10-13
申请号：US11256471
申请日：2005-10-21
申请人： Lin Hong , Hong Shen , Shuping Qing
发明人： Lin Hong , Hong Shen , Shuping Qing
IPC分类号： G06K9/00
CPC分类号： G06K9/00201 , G06T7/0012
摘要： In a method of 3-D object detection, an image is pre-processed. Using cross-section analysis, a confidence array is built. A plurality of peaks in the confidence array are detected, wherein the peaks signify a likelihood of a 3-D object of interest.
摘要翻译：在三维物体检测的方法中，图像被预处理。使用横截面分析，构建置信阵列。检测置信阵列中的多个峰，其中峰表示感兴趣的3-D物体的可能性。

5. 发明申请

US20080285830A1 Computer Aided Lesion Assessment in Dynamic Contrast Enhanced Breast MRI Images 有权
标题翻译：动态对比增强乳腺MRI图像的计算机辅助病变评估
公开(公告)号：US20080285830A1
公开(公告)日：2008-11-20
申请号：US12117155
申请日：2008-05-08
申请人： Lin Hong , Hong Shen , Shuping Qing
发明人： Lin Hong , Hong Shen , Shuping Qing
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T2207/10096 , G06T2207/30096
摘要： A method for performing computer-assisted lesion assessment includes acquiring contrast enhanced MRI data. A region of interest is identified. Kinetic properties, morphological properties and pattern of enhancement are determined for the identified region of interest. A score is calculated for each of the kinetic properties, morphological properties, pattern of enhancement. A cumulative score is calculated for the identified region of interest. The scores are provided to a medical practitioner to assist in diagnosing the identified region of interest.
摘要翻译：用于执行计算机辅助病变评估的方法包括获取对比度增强的MRI数据。确定感兴趣的区域。确定感兴趣区域的动力学性质，形态特征和增强模式。计算每个动力学性质，形态特征，增强模式的得分。计算所识别的感兴趣区域的累积分数。分数被提供给医生以帮助诊断所识别的感兴趣的区域。

6. 发明申请

US20060093217A1 Object detection using cross-section analysis 失效
公开(公告)号：US20060093217A1
公开(公告)日：2006-05-04
申请号：US11256471
申请日：2005-10-21
申请人： Lin Hong , Hong Shen , Shuping Qing
发明人： Lin Hong , Hong Shen , Shuping Qing
IPC分类号： G06K9/00
CPC分类号： G06K9/00201 , G06T7/0012
摘要： In a method of 3-D object detection, an image is pre-processed. Using cross-section analysis, a confidence array is built. A plurality of peaks in the confidence array are detected, wherein the peaks signify a likelihood of a 3-D object of interest.

7. 发明申请

US20090041328A1 Feature Processing For Lung Nodules In Computer Assisted Diagnosis 有权
标题翻译：计算机辅助诊断中肺结节特征处理
公开(公告)号：US20090041328A1
公开(公告)日：2009-02-12
申请号：US12170639
申请日：2008-07-10
申请人： Lin Hong , Christopher V. Alvino , Hong Shen
发明人： Lin Hong , Christopher V. Alvino , Hong Shen
IPC分类号： G06K9/62
CPC分类号： G06T7/0012 , G06T2207/30061 , G06T2207/30064
摘要： Feature processing is provided for lung nodules in computer-assisted diagnosis. A feature that may better distinguish nodules from background is extracted using a Hough transform. Rather than relying on a specific boundary shape, the Hough transform accumulates evidence associated with a region, such as a ring region. The accumulated evidence provides a feature score without requiring a nodule to fit a specific shape. In another approach, a background level is determined from extracted features. Rather than attempting to normalize an image prior to extraction, the features are normalized. The feature normalization and generalized Hough transform extraction may be used together or alone.
摘要翻译：在计算机辅助诊断中为肺结节提供特征处理。使用霍夫变换提取可以更好地区分结节与背景的特征。霍夫变换不是依赖于特定的边界形状，而是累积与区域相关联的证据，例如环形区域。积累的证据提供了一个特征分数，而不需要结节来适应特定的形状。在另一种方法中，从提取的特征确定背景级别。在尝试在提取之前对图像进行归一化，而不是将特征归一化。特征归一化和广义霍夫变换提取可以一起使用或单独使用。

8. 发明授权

US07702153B2 Systems and methods for segmenting object of interest from medical image 有权
标题翻译：从医学图像分割感兴趣对象的系统和方法
公开(公告)号：US07702153B2
公开(公告)日：2010-04-20
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： G06K9/34
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。

9. 发明申请

US20090087072A1 Method and system for diaphragm segmentation in chest X-ray radiographs 有权
标题翻译：胸部X光片检查的隔膜分割方法及系统
公开(公告)号：US20090087072A1
公开(公告)日：2009-04-02
申请号：US12284136
申请日：2008-09-18
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： G06T7/149 , G06T7/12 , G06T2207/10116 , G06T2207/20012 , G06T2207/20036 , G06T2207/30061
摘要： A method and system for segmenting diaphragm regions in a chest X-ray radiograph is disclosed. The diaphragm regions are segmented based on left and right lung lobe regions and a heart region in the chest X-ray radiograph. A right diaphragm region is segmented in the chest X-ray radiograph based a boundary of the right lung lobe. A left diaphragm region is segmented in the chest X-ray radiograph based on the heart region and a boundary of the left lung lobe.
摘要翻译：公开了一种用于分割胸部X线片的隔膜区域的方法和系统。基于左肺和右肺叶区域以及胸部X线片中的心脏区域分隔隔膜区域。基于右肺叶的边界，在胸部X射线照片中分割右侧隔膜区域。基于心脏区域和左肺叶的边界在胸部X射线照片中分割左隔膜区域。

10. 发明授权

US08224057B2 Method and system for nodule feature extraction using background contextual information in chest x-ray images 有权
标题翻译：使用胸部x射线图像中的背景情境信息进行结节特征提取的方法和系统
公开(公告)号：US08224057B2
公开(公告)日：2012-07-17
申请号：US12286968
申请日：2008-10-03
申请人： Lin Hong , Hong Shen
发明人： Lin Hong , Hong Shen
IPC分类号： G06K9/46 , G06K9/78 , G06T7/60 , G01N23/04
CPC分类号： G06T7/0012 , G06K2009/00932 , G06K2209/051 , G06K2209/053 , G06T2207/10116 , G06T2207/30064 , Y10S378/901
摘要： A method and system for nodule feature extract using background contextual information in chest x-ray images is disclosed. In order to detect false positives in nodule candidates for a chest x-ray image, background contextual information, such as contextual vessel tree information, is defined in the chest x-ray image. Features are extracted for each nodule candidate based on the background contextual information, and the extracted features are used to detect whether each nodule candidate is a false positive or a genuine nodule.
摘要翻译：公开了使用胸部X射线图像中的背景背景信息的结节特征提取的方法和系统。为了检测胸部x射线图像的结节候选物中的假阳性，在胸部x射线图像中定义了背景上下文信息，例如上下文血管树信息。基于背景上下文信息为每个结节候选提取特征，并且提取的特征用于检测每个结节候选物是假阳性还是真正的结节。

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