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1. 发明申请

US20050207630A1 Lung nodule detection and classification 审中-公开
标题翻译：肺结核检测和分类
公开(公告)号：US20050207630A1
公开(公告)日：2005-09-22
申请号：US10504197
申请日：2003-02-14
申请人： Heang-Ping Chan , Berkman Sahiner , Lubomir Hadjiyski , Chuan Zhou , Nicholas Petrick
发明人： Heang-Ping Chan , Berkman Sahiner , Lubomir Hadjiyski , Chuan Zhou , Nicholas Petrick
IPC分类号： A61B6/03 , G06F19/00 , G06K9/00 , G06T7/00
CPC分类号： G06T7/0012 , A61B6/03 , A61B6/466 , A61B6/583 , G06T2207/10081 , G06T2207/30061
摘要： A computer assisted method of detecting and classifying lung nodules within a set of CT images includes performing body contour, airway, lung and esophagus segmentation to identify the regions of the CT images in which to search for potential lung nodules. The lungs are processed to identify the left and right sides of the lungs and each side of the lung is divided into subregions including upper, middle and lower subregions and central, intermediate and peripheral subregions. The computer analyzes each of the lung regions to detect and identify a three-dimensional vessel tree representing the blood vessels at or near the mediastinum. The computer then detects objects that are attached to the lung wall or to the vessel tree to assure that these objects are not eliminated from consideration as potential nodules. Thereafter, the computer performs a pixel similarity analysis on the appropriate regions within the CT images to detect potential nodules and performs one or more expert analysis techniques using the features of the potential nodules to determine whether each of the potential nodules is or is not a lung nodule. Thereafter, the computer uses further features, such as speculation features, growth features, etc. in one or more expert analysis techniques to classify each detected nodule as being either benign or malignant. The computer then displays the detection and classification results to the radiologist to assist the radiologist in interpreting the CT exam for the patient.
摘要翻译：在一组CT图像中检测和分类肺结节的计算机辅助方法包括执行身体轮廓，气道，肺和食管分割，以识别CT图像的搜索潜在肺结节的区域。处理肺以识别肺的左侧和右侧，并且将肺的每一侧分为包括上部，中部和下部亚区域以及中央，中间和周边子区域的子区域。计算机分析每个肺部区域以检测和识别表示纵隔处或附近的血管的三维血管。然后，计算机检测附着到肺壁或血管树上的物体，以确保这些物体不被考虑为潜在的结节。此后，计算机对CT图像内的适当区域进行像素相似性分析以检测潜在的结节，并使用潜在结节的特征来执行一个或多个专家分析技术，以确定每个潜在结节是否为肺结核。此后，计算机在一个或多个专家分析技术中使用进一步的特征，例如推测特征，生长特征等，以将每个检测到的结节分类为良性或恶性。然后，计算机将检测和分类结果显示给放射科医师，以协助放射科医师解释患者的CT检查。

2. 发明申请

US20090252395A1 System and Method of Identifying a Potential Lung Nodule 审中-公开
标题翻译：识别潜在肺结节的系统和方法
公开(公告)号：US20090252395A1
公开(公告)日：2009-10-08
申请号：US12484941
申请日：2009-06-15
申请人： Heang-Ping Chan , Berkman Sahiner , Lubomir M. Hadjiyski , Chuan Zhou , Nicholas Petrick
发明人： Heang-Ping Chan , Berkman Sahiner , Lubomir M. Hadjiyski , Chuan Zhou , Nicholas Petrick
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , A61B6/03 , A61B6/466 , A61B6/583 , G06T2207/10081 , G06T2207/30061
摘要： A computer assisted method of detecting and classifying lung nodules within a set of CT images to identify the regions of the CT images in which to search for potential lung nodules. The lungs are processed to identify a subregion of a lung on a CT image. The computer defines a nodule centroid for a nodule class of pixels and a background centroid for a background class of pixels within the subregion in the CT image; and determines a nodule distance between a pixel and the nodule centroid and a background distance between the pixel and the background centroid. Thereafter, the computer assigns the pixel to the nodule class or to the background class based on the first and second distances; stores the identification in a memory; and analyzes the nodule class to determine the likelihood of each pixel cluster being a true nodule.
摘要翻译：一种在一组CT图像内检测和分类肺结节的计算机辅助方法，以识别在其中搜索潜在肺结节的CT图像的区域。处理肺以识别CT图像上的肺的亚区域。计算机为CT图像中的子区域内的像素的背景类别定义了结节类像素的结节重心和背景质心; 并确定像素与结节重心之间的结节距离以及像素与背景质心之间的背景距离。此后，计算机基于第一和第二距离将像素分配给结节类或背景类; 将识别存储在存储器中; 并分析结节类以确定每个像素簇是真实结节的可能性。

3. 发明申请

US20120294502A1 Methods for Microalification Detection of Breast Cancer on Digital Tomosynthesis Mammograms 有权
标题翻译：乳腺癌细胞微量化检测方法
公开(公告)号：US20120294502A1
公开(公告)日：2012-11-22
申请号：US13514804
申请日：2011-02-11
申请人： Heang-Ping Chan , Berkman Sahiner , Lubomir M. Hadjiiski , Jun Wei , Mark A. Helvie
发明人： Heang-Ping Chan , Berkman Sahiner , Lubomir M. Hadjiiski , Jun Wei , Mark A. Helvie
IPC分类号： G06K9/62
CPC分类号： G06T7/0012 , A61B6/025 , A61B6/502 , A61B6/5211 , G06T2207/10112 , G06T2207/30068
摘要： A computer-aided detection system to detect clustered microcalcifications in digital breast tomosynthesis (DBT) is disclosed. The system performs detection in 2D images and a reconstructed 3D volume. The system may include an initial prescreening of potential microcalcifications by using one or more 3D calcification response function (CRF) values modulated by an enhancement method to identify high response locations in the DBT volume as potential signals. Microcalcifications may be enhanced using a Multi-Channel Enhancement method. Locations detected using these methods can be identified and the potential microcalcifications may be extracted. The system may include object segmentation that uses region growing guided by the enhancement-modulated CRF values, gray level voxel values relative to a local background level, or the original DBT voxel values. False positives may be reduced by descriptors of characteristics of microcalcifications. Detected locations of clusters and a cluster significance rating of each cluster may be output and displayed.
摘要翻译：公开了一种用于检测数字乳房断层合成（DBT）中的聚类微钙化的计算机辅助检测系统。系统在2D图像和重建的3D体积中执行检测。该系统可以包括通过使用由增强方法调制的一个或多个3D钙化响应函数（CRF）值来识别潜在微钙化的初始预筛选，以将DBT体积中的高响应位置识别为潜在信号。可以使用多通道增强方法来增强微钙化。可以识别使用这些方法检测的位置，并且可以提取潜在的微钙化。系统可以包括使用由增强调制CRF值引导的区域生长，相对于本地背景水平的灰度体素值或原始DBT体素值的对象分割。微量钙化特征的描述可能会降低假阳性。可以输出并显示簇的检测位置和每个簇的簇重要性等级。

4. 发明授权

US08977019B2 Methods for microcalcification detection of breast cancer on digital tomosynthesis mammograms 有权
标题翻译：乳腺癌微钙化检测方法在数字断层合成乳房X光检查中的应用
公开(公告)号：US08977019B2
公开(公告)日：2015-03-10
申请号：US13514804
申请日：2011-02-11
申请人： Heang-Ping Chan , Berkman Sahiner , Lubomir M. Hadjiiski , Jun Wei , Mark A. Helvie
发明人： Heang-Ping Chan , Berkman Sahiner , Lubomir M. Hadjiiski , Jun Wei , Mark A. Helvie
IPC分类号： G06K9/00 , G06T7/00 , A61B6/02 , A61B6/00
CPC分类号： G06T7/0012 , A61B6/025 , A61B6/502 , A61B6/5211 , G06T2207/10112 , G06T2207/30068
摘要： A computer-aided detection system to detect clustered microcalcifications in digital breast tomosynthesis (DBT) is disclosed. The system performs detection in 2D images and a reconstructed 3D volume. The system may include an initial prescreening of potential microcalcifications by using one or more 3D calcification response function (CRF) values modulated by an enhancement method to identify high response locations in the DBT volume as potential signals. Microcalcifications may be enhanced using a Multi-Channel Enhancement method. Locations detected using these methods can be identified and the potential microcalcifications may be extracted. The system may include object segmentation that uses region growing guided by the enhancement-modulated CRF values, gray level voxel values relative to a local background level, or the original DBT voxel values. False positives may be reduced by descriptors of characteristics of microcalcifications. Detected locations of clusters and a cluster significance rating of each cluster may be output and displayed.
摘要翻译：公开了一种用于检测数字乳房断层合成（DBT）中的聚类微钙化的计算机辅助检测系统。系统在2D图像和重建的3D体积中执行检测。该系统可以包括通过使用由增强方法调制的一个或多个3D钙化响应函数（CRF）值来识别潜在微钙化的初始预筛选，以将DBT体积中的高响应位置识别为潜在信号。可以使用多通道增强方法来增强微钙化。可以识别使用这些方法检测的位置，并且可以提取潜在的微钙化。系统可以包括使用由增强调制CRF值引导的区域生长，相对于本地背景水平的灰度体素值或原始DBT体素值的对象分割。微量钙化特征的描述可能会降低假阳性。可以输出并显示簇的检测位置和每个簇的簇重要性等级。

5. 发明申请

US20100104154A1 Computerized Detection of Breast Cancer on Digital Tomosynthesis Mamograms 审中-公开
标题翻译：计算机化检测乳腺癌数字化合成Mamograms
公开(公告)号：US20100104154A1
公开(公告)日：2010-04-29
申请号：US12624273
申请日：2009-11-23
申请人： Heang-Ping Chan , Jun Wei , Berkman Sahiner , Lubomir M. Hadjiiski
发明人： Heang-Ping Chan , Jun Wei , Berkman Sahiner , Lubomir M. Hadjiiski
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06K9/00 , G06K2209/05 , G06T7/44 , G06T2207/10081 , G06T2207/30068
摘要： A method for using computer-aided diagnosis (CAD) for digital tomosynthesis mammograms (DTM) including retrieving a DTM image file having a plurality of DTM image slices; applying a three-dimensional analysis to the DTM image file to detect lesion candidates; identifying a volume of interest and locating its center; segmenting the volume of interest by a three dimensional method; extracting one or more object characteristics from the object corresponding to the volume of interest; and determining if the object corresponding to the volume of interest is a breast lesion or normal breast tissue.
摘要翻译：一种用于计算机辅助诊断（CAD）用于数字断层合成乳房X线照片（DTM）的方法，包括检索具有多个DTM图像切片的DTM图像文件; 对DTM图像文件应用三维分析来检测病变候选者; 识别感兴趣的体积和定位其中心; 通过三维方法分割感兴趣的体积; 从对应于感兴趣体积的对象提取一个或多个对象特征; 并且确定与感兴趣体积相对应的物体是否为乳房病变或正常乳腺组织。

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