专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

61. 发明申请

US20110021915A1 Detection of Structure in Ultrasound M-Mode Imaging 有权
标题翻译：超声M模式成像检测结构
公开(公告)号：US20110021915A1
公开(公告)日：2011-01-27
申请号：US12839547
申请日：2010-07-20
申请人： Shaolei Feng , Wei Zhang , Shaohua Kevin Zhou , Jin-hyeong Park
发明人： Shaolei Feng , Wei Zhang , Shaohua Kevin Zhou , Jin-hyeong Park
IPC分类号： A61B8/14
CPC分类号： A61B8/08 , A61B5/107 , A61B5/7267 , A61B8/0883 , A61B8/461 , A61B8/463 , A61B8/465 , A61B8/486 , G06T7/73 , G06T2207/10132 , G06T2207/30048
摘要： Automated detection of structure is provided in ultrasound M-mode imaging. A coarse and fine search for structure is used. For example, a less noise susceptible initial position or range of positions for a given structure is determined. This position is then refined. The coarse positioning and/or the refined position may use machine-trained classifiers. The positions of other structure may be used in either coarse or fine positioning, such as using a Markov Random Field. The structure or structures may be identified in the M-mode image without user input of a location in the M-mode image or along the line.
摘要翻译：在超声M模式成像中提供了结构的自动检测。使用粗略和精细的结构搜索。例如，确定给定结构的较小噪声敏感的初始位置或位置范围。然后改善这一立场。粗略定位和/或精细位置可以使用机器训练的分类器。其他结构的位置可以用于粗略或精细定位，例如使用马尔可夫随机场。可以在M模式图像中识别结构或结构，而无需用户输入M模式图像中的位置或沿着线。

62. 发明授权

US08343053B2 Detection of structure in ultrasound M-mode imaging 有权
标题翻译：超声M模式成像检测结构
公开(公告)号：US08343053B2
公开(公告)日：2013-01-01
申请号：US12839547
申请日：2010-07-20
申请人： Shaolei Feng , Wei Zhang , Shaohua Kevin Zhou , Jin-hyeong Park
发明人： Shaolei Feng , Wei Zhang , Shaohua Kevin Zhou , Jin-hyeong Park
IPC分类号： A61B8/00 , G06K9/00 , G06E1/00
CPC分类号： A61B8/08 , A61B5/107 , A61B5/7267 , A61B8/0883 , A61B8/461 , A61B8/463 , A61B8/465 , A61B8/486 , G06T7/73 , G06T2207/10132 , G06T2207/30048
摘要： Automated detection of structure is provided in ultrasound M-mode imaging. A coarse and fine search for structure is used. For example, a less noise susceptible initial position or range of positions for a given structure is determined. This position is then refined. The coarse positioning and/or the refined position may use machine-trained classifiers. The positions of other structure may be used in either coarse or fine positioning, such as using a Markov Random Field. The structure or structures may be identified in the M-mode image without user input of a location in the M-mode image or along the line.
摘要翻译：在超声M模式成像中提供了结构的自动检测。使用粗略和精细的结构搜索。例如，确定给定结构的较小噪声敏感的初始位置或位置范围。然后改善这一立场。粗略定位和/或精细位置可以使用机器训练的分类器。其他结构的位置可以用于粗略或精细定位，例如使用马尔可夫随机场。可以在M模式图像中识别结构或结构，而无需用户输入M模式图像中的位置或沿着线。

63. 发明申请

US20130177230A1 IMPLANT POSE DETERMINATION IN MEDICAL IMAGING 有权
标题翻译：在医学成像中的植入位置确定
公开(公告)号：US20130177230A1
公开(公告)日：2013-07-11
申请号：US13735112
申请日：2013-01-07
申请人： Shaolei Feng , Shaohua Kevin Zhou , Gerhard Kleinszig , Rainer Graumann
发明人： Shaolei Feng , Shaohua Kevin Zhou , Gerhard Kleinszig , Rainer Graumann
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06T7/75 , G06T2207/10116 , G06T2207/30052
摘要： The pose of an implant represented in a medical image is determined from the medical image. The x-ray image of the implant is compared to a database of the implant viewed at different poses (e.g., viewed from different directions). The implant pose associated with the best match indicates the pose of the implant in the x-ray image.
摘要翻译：根据医学图像确定在医学图像中表示的植入物的姿势。将植入物的x射线图像与在不同姿势（例如从不同方向观察）观察的植入物的数据库进行比较。与最佳匹配关联的植入姿势表示植入物在X射线图像中的姿态。

64. 发明授权

US08407267B2 Apparatus, method, system and computer-readable medium for storing and managing image data 有权
标题翻译：用于存储和管理图像数据的装置，方法，系统和计算机可读介质
公开(公告)号：US08407267B2
公开(公告)日：2013-03-26
申请号：US12554436
申请日：2009-09-04
申请人： Johannes Feulner , Shaohua Kevin Zhou
发明人： Johannes Feulner , Shaohua Kevin Zhou
IPC分类号： G06F17/30
CPC分类号： G06F19/321 , G06F19/00
摘要： An apparatus, method, system and computer-readable medium store and manage image data with automatic labeling of image data corresponding to body slices, such as obtained by a computed tomography scanner. The labels include a body coordinate value along the body axis. The respective body coordinate value can be determined by comparing received image data sets with reference data sets with known attached coordinate values utilizing pattern recognition techniques. Applications include medical image data management in hospitals or operating and providing medical networks. Queries for images that include particular body regions are processed more efficiently. This results in less local memory required and narrower bandwidth resources of transmission networks.
摘要翻译：一种装置，方法，系统和计算机可读介质利用计算机断层摄影扫描器获得的对身体切片的图像数据进行自动标注来存储和管理图像数据。标签包括身体轴线上的身体坐标值。可以通过使用模式识别技术将接收到的图像数据集与具有已知附加坐标值的参考数据集进行比较来确定相应的身体坐标值。应用包括医院的医学图像数据管理或运营和提供医疗网络。对包含特定身体区域的图像的查询进行更有效的处理。这导致需要较少的本地存储器和较窄的传输网络的带宽资源。

65. 发明申请

US20120106810A1 Automated Rib Ordering and Pairing 有权
标题翻译：自动排序和配对
公开(公告)号：US20120106810A1
公开(公告)日：2012-05-03
申请号：US13274515
申请日：2011-10-17
申请人： Sowmya Ramakrishnan , Christopher V. Alvino , Dijia Wu , David Liu , Shaohua Kevin Zhou
发明人： Sowmya Ramakrishnan , Christopher V. Alvino , Dijia Wu , David Liu , Shaohua Kevin Zhou
IPC分类号： G06K9/00
CPC分类号： G06T7/73 , G06T2207/10081 , G06T2207/30012 , G06T2207/30064 , G06T2207/30172
摘要： Ribs are automatically ordered and paired. After ordering ribs on each side, magnetic and spring functions are used to solve for rib pairing. The magnetic function is used to constrain possible pairs across sides, and the spring function is used to maintain the order on each side while accounting for missing or fused ribs.
摘要翻译：肋骨自动排序并配对。在每侧排列肋后，使用磁力和弹簧功能来解决肋配对。磁功能用于限制双方的可能对，弹簧功能用于维护每侧的顺序，同时考虑到缺失或融合的肋骨。

66. 发明申请

US20120070074A1 Method and System for Training a Landmark Detector using Multiple Instance Learning 有权
标题翻译：使用多实例学习训练地标检测器的方法和系统
公开(公告)号：US20120070074A1
公开(公告)日：2012-03-22
申请号：US13228509
申请日：2011-09-09
申请人： David Liu , Shaohua Kevin Zhou
发明人： David Liu , Shaohua Kevin Zhou
IPC分类号： G06K9/62
CPC分类号： G06K9/6257 , G06K2209/051
摘要： An apparatus and method for training a landmark detector receives training data which includes a plurality of positive training bags, each including a plurality of positively annotated instances, and a plurality of negative training bags, each including at least one negatively annotated instance. Classification function is initialized by training a first weak classifier based on the positive training bags and the negative training bags. All training instances are evaluated using the classification function. For each of a plurality of remaining classifiers, a cost value gradient is calculated based on spatial context information of each instance in each positive bag evaluated by the classification function. A gradient value associated with each of the remaining weak classifiers is calculated based on the cost value gradients, and a weak classifier is selected which has a lowest associated gradient value and given a weighting parameter and added to the classification function.
摘要翻译：用于训练地标检测器的装置和方法接收训练数据，训练数据包括多个正训练袋，每个正训练袋包括多个带有正面注释的实例，以及多个负训练袋，每个包括至少一个负注释实例。基于积极的训练袋和负面训练袋训练第一个弱分类器来初始化分类功能。使用分类函数评估所有训练实例。对于多个剩余分类器中的每一个，基于由分类函数评估的每个正包中的每个实例的空间上下文信息来计算成本值梯度。基于成本值梯度计算与剩余弱分类器中的每一个相关联的梯度值，并且选择具有最低相关梯度值并给出加权参数并加到分类函数的弱分类器。

67. 发明授权

US09639637B2 Construction of entropy-based prior and posterior probability distributions with partial information for fatigue damage prognostics 有权
公开(公告)号：US09639637B2
公开(公告)日：2017-05-02
申请号：US14015084
申请日：2013-08-30
申请人： Xuefei Guan , Jingdan Zhang , Shaohua Kevin Zhou
发明人： Xuefei Guan , Jingdan Zhang , Shaohua Kevin Zhou
IPC分类号： G06F7/60 , G06F17/10 , G06F17/50
CPC分类号： G06F17/5009 , G06F2217/10 , G06F2217/76
摘要： A method for predicting fatigue crack growth in materials includes providing a prior distribution obtained using response measures from one or more target components using a fatigue crack growth model as a constraint function, receiving new crack length measurements, providing a posterior distribution obtained using the new crack length measurements, and sampling the posterior distribution to obtain crack length measurement predictions.

68. 发明授权

US09117259B2 Method and system for liver lesion detection 有权
标题翻译：肝脏病变检测方法与系统
公开(公告)号：US09117259B2
公开(公告)日：2015-08-25
申请号：US13238370
申请日：2011-09-21
申请人： David Liu , Dijia Wu , Shaohua Kevin Zhou , Maria Jimena Costa , Michael Suehling , Christian Tietjen
发明人： David Liu , Dijia Wu , Shaohua Kevin Zhou , Maria Jimena Costa , Michael Suehling , Christian Tietjen
IPC分类号： G06K9/00 , G06T7/00 , G06T7/40
CPC分类号： G06T7/0016 , G06T7/41 , G06T2207/10072 , G06T2207/20012 , G06T2207/20041 , G06T2207/20081 , G06T2207/20104 , G06T2207/30056
摘要： A method and system for automatically detecting liver lesions in medical image data, such as 3D CT images, is disclosed. A liver region is segmented in a 3D image. Liver lesion center candidates are detected in the segmented liver region. Lesion candidates are segmented corresponding to the liver lesion center candidates, and lesions are detected from the segmented lesion candidates using learning based verification.
摘要翻译：公开了一种用于自动检测医学图像数据中肝脏病变的方法和系统，如3D CT图像。肝脏区域被分割成3D图像。肝脏病变中心候选人在分段肝脏区域检测到。病变候选者对应于肝脏病变中心候选物，并且使用基于学习的验证从分割的病变候选物检测病变。

69. 发明授权

US09113781B2 Method and system for on-site learning of landmark detection models for end user-specific diagnostic medical image reading 有权
标题翻译：用于最终用户特定诊断医学图像阅读的地标检测模型的现场学习方法和系统
公开(公告)号：US09113781B2
公开(公告)日：2015-08-25
申请号：US13761263
申请日：2013-02-07
申请人： Michael Wels , Michael Kelm , Michael Suehling , Shaohua Kevin Zhou
发明人： Michael Wels , Michael Kelm , Michael Suehling , Shaohua Kevin Zhou
IPC分类号： G06K9/00 , A61B5/00 , A61B6/00 , G06K9/62 , G06F19/00
CPC分类号： A61B5/0033 , A61B6/00 , A61B6/025 , A61B6/502 , G06F19/00 , G06F19/321 , G06K9/00 , G06K9/6255 , G06K2209/05
摘要： A method and system for on-line learning of landmark detection models for end-user specific diagnostic image reading is disclosed. A selection of a landmark to be detected in a 3D medical image is received. A current landmark detection result for the selected landmark in the 3D medical image is determined by automatically detecting the selected landmark in the 3D medical image using a stored landmark detection model corresponding to the selected landmark or by receiving a manual annotation of the selected landmark in the 3D medical image. The stored landmark detection model corresponding to the selected landmark is then updated based on the current landmark detection result for the selected landmark in the 3D medical image. The landmark selected in the 3D medical image can be a set of landmarks defining a custom view of the 3D medical image.
摘要翻译：公开了一种用于在线学习用于最终用户特定诊断图像读取的地标检测模型的方法和系统。接收在3D医疗图像中检测的地标的选择。通过使用与所选择的地标相对应的存储的地标检测模型自动检测3D医学图像中的所选地标来确定3D医学图像中所选择的地标的当前地标检测结果，或者通过接收所选地标的手动注释 3D医学图像。然后基于3D医学图像中所选地标的当前地标检测结果来更新对应于所选地标的所存储的地标检测模型。在3D医学图像中选择的地标可以是定义3D医学图像的定制视图的一组地标。

70. 发明授权

US08699817B2 Reconstruction of phased array data 失效
标题翻译：相控阵数据重建
公开(公告)号：US08699817B2
公开(公告)日：2014-04-15
申请号：US13245003
申请日：2011-09-26
申请人： Jingdan Zhang , Moritz Michael Knorr , Guozhen Li , Shaohua Kevin Zhou , El Mahjoub Rasselkorde , Waheed A. Abbasi , Michael J. Metala
发明人： Jingdan Zhang , Moritz Michael Knorr , Guozhen Li , Shaohua Kevin Zhou , El Mahjoub Rasselkorde , Waheed A. Abbasi , Michael J. Metala
IPC分类号： G06K9/36
CPC分类号： G06T17/00
摘要： An image reconstruction method includes receiving volume data comprising a plurality of sampling points, determining a first conditioning of the sampling points suppressing low amplitudes and conserving maximum amplitudes, determining a second conditioning of the sampling points wherein an influence of a sampling point depends on its distance to a grid point in a sampling grid, determining a kernel comprising a plurality of weighting functions for the first conditioning and the second conditioning to determine an energy spread of each of the plurality of sampling points without determining a shape or size of the kernel, and outputting a reconstructed volume according to the energy spread of each of the plurality of sampling points.
摘要翻译：图像重建方法包括：接收包含多个采样点的音量数据，确定抑制低振幅的采样点和节省最大振幅的第一调节，确定采样点的影响取决于其距离的采样点的第二调节到采样网格中的网格点，确定包含用于所述第一调节的多个加权函数的内核和所述第二调节，以确定所述多个采样点中的每一个的能量扩展，而不确定所述内核的形状或大小，以及根据所述多个采样点中的每一个的能量扩展输出重构的音量。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式