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21. 发明申请

US20090080740A1 Automatic Lesion Detection and Characterization Using Generative Model of Enhancement Dynamics in Breast MR 审中-公开
标题翻译：自动病变检测和表征使用乳房MR增强动力学的生成模型
公开(公告)号：US20090080740A1
公开(公告)日：2009-03-26
申请号：US12205114
申请日：2008-09-05
申请人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
发明人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
IPC分类号： G06K9/00
CPC分类号： G01R33/5601 , G01R33/5608 , G06K9/38 , G06K2209/05 , G06T7/0012 , G06T2207/10088 , G06T2207/10096 , G06T2207/30004
摘要： A method for automatic detection of lesions within MR images includes administering a magnetic contrast agent into a subject. A sequence of MR images are acquired at predetermined intervals of time. One or more regions of suspicion are automatically identified within the MR images. A bidirectional exchange of the magnetic contrast agent between each compartment and its neighboring compartment is monitored for each compartment within each region of suspicion. Each region of suspicion is characterized based on the bidirectional exchange between each of its compartments and their neighboring compartments.
摘要翻译：用于自动检测MR图像内的损伤的方法包括向受试者施用磁性造影剂。以预定的时间间隔获取MR图像序列。在MR图像内自动识别一个或多个怀疑区域。监测每个隔间与其相邻隔室之间的磁性造影剂的双向交换，以便在每个怀疑区域内的每个隔间。基于每个隔间与其相邻隔室之间的双向交换来表征每个怀疑区域。

22. 发明申请

US20090069666A1 Correction of Intensity Inhomogeneity in Breast MRI 审中-公开
标题翻译：乳腺MRI强度不均匀性的校正
公开(公告)号：US20090069666A1
公开(公告)日：2009-03-12
申请号：US12205220
申请日：2008-09-05
申请人： Gerardo Hermosillo Valadez , Yoshihisa Shinagawa
发明人： Gerardo Hermosillo Valadez , Yoshihisa Shinagawa
IPC分类号： A61B5/055 , A61B5/00
CPC分类号： G01R33/56563 , G01R33/281 , G01R33/5608
摘要： A method for correcting for magnetic field inhomogeneity in a breast MRI, includes acquiring a magnetic resonance (MR) image including a patients breast. A surface image of the breast is automatically isolated within the acquired MR image based on a high contrast between the breast and surrounding air. A multiplicative field that transforms the isolated surface image into a uniform intensity is generated. The generated multiplicative field is applied to the entire acquired MR image to produce a corrected image.
摘要翻译：一种用于校正乳房MRI中的磁场不均匀性的方法，包括获取包括患者乳房的磁共振（MR）图像。基于乳房和周围空气之间的高对比度，在所获取的MR图像内自动隔离乳房的表面图像。产生将孤立表面图像变换为均匀强度的乘法场。生成的乘法场被施加到整个获取的MR图像以产生校正图像。

23. 发明授权

US08331636B2 Automatic calibration of computer aided diagnosis based on retrospective examination 有权
标题翻译：基于回顾性检查的计算机辅助诊断的自动校准
公开(公告)号：US08331636B2
公开(公告)日：2012-12-11
申请号：US12204870
申请日：2008-09-05
申请人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
发明人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
IPC分类号： G06K9/00
CPC分类号： G06T7/0012
摘要： A method for automatic detection of lesions within a medical image include acquiring medical image data. Regions of suspicion are automatically identified within the medical image data. It is automatically determined whether each identified region of suspicion is of a benign state, is of a suspicious state that requires a biopsy, or is of an indeterminate state that requires subsequent imaging after a particular length of time. When an identified region of suspicion is determined to be of an indeterminate state, the determination is automatically reconsidered in light of a calibration factor that biases the automatic determination towards either a benign state or a suspicious state. The calibration factor may be based on data collected from follow-up examinations that reveal whether a lesion previously characterized as indeterminate was actually a benign or malignant lesion or on additional diagnostic information including prior image data or non-image data.
摘要翻译：用于自动检测医学图像内的病变的方法包括获取医学图像数据。怀疑区域在医学图像数据内自动识别。自动确定每个识别的怀疑区域是否处于良性状态，是需要活组织检查的可疑状态，或者是在特定时间长度之后需要后续成像的不确定状态。当识别出的怀疑区域被确定为不确定状态时，根据将自动判定偏向良性状态或可疑状态的校准因素自动重新确定该确定。校准因子可以基于从随访检查中收集的数据，其揭示先前特征为不确定性的病变实际上是良性或恶性病变，还是包括先前图像数据或非图像数据的附加诊断信息。

24. 发明授权

US08144953B2 Multi-scale analysis of signal enhancement in breast MRI 有权
标题翻译：乳腺MRI信号增强的多尺度分析
公开(公告)号：US08144953B2
公开(公告)日：2012-03-27
申请号：US12206804
申请日：2008-09-09
申请人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
发明人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
IPC分类号： G06K9/00
CPC分类号： G01R33/5601 , G01R33/5608 , G06K9/4609 , G06K2209/05 , G06T5/50 , G06T7/0012 , G06T7/11 , G06T7/174 , G06T2207/10088 , G06T2207/20224 , G06T2207/30068 , G06T2207/30096
摘要： A method for computer assisted lesion detection in magnetic resonance (MR) images includes acquiring dynamic contrast enhanced (DCE) MR images. The images are processed to produce a subtraction image illustrating change in voxel enhancement between the images. A Gaussian low-pass filter is applied to the subtraction image. An elimination mask is created from the filtered subtraction image by removing voxels with enhancement values below a threshold value. The elimination mask is used to remove noise from the subtraction image. One or more regions of suspicion are automatically detected from the noise-removed subtraction image. To produce the subtraction image, DCE-MR images are divided into first and second sub-sets. Positive-signed enhancement values of voxels of the MR images from the first sub-set are added to a combined subtraction image along with absolute values of all enhancement values of voxels of the MR images from the second sub-set.
摘要翻译：用于磁共振（MR）图像中计算机辅助病变检测的方法包括采集动态对比度增强（DCE）MR图像。图像被处理以产生减影图像，说明图像之间的体素增强的变化。将高斯低通滤波器应用于减法图像。通过去除具有低于阈值的增强值的体素，从滤波的减法图像创建消除掩模。消除掩模用于从减影图像中去除噪声。从噪声消除的减影图像中自动检测一个或多个怀疑区域。为了产生减法图像，DCE-MR图像被分成第一和第二子集。来自第一子集的MR图像的体素的正号增强值与来自第二子集的MR图像的体素的所有增强值的绝对值相加到组合减影图像。

25. 发明授权

US08135188B2 Efficient features for shape analysis of lesions in breast MR 有权
标题翻译：乳腺MR病变形状分析的有效特征
公开(公告)号：US08135188B2
公开(公告)日：2012-03-13
申请号：US12204953
申请日：2008-09-05
申请人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
发明人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06K9/469 , G06T2207/10088 , G06T2207/20072 , G06T2207/30068
摘要： A method for analyzing a shape of a region of interest in a medical image of a body part, including: finding a region of interest in the medical image; calculating a Reeb graph of the region of interest, and determining whether the region of interest is a malignant lesion candidate based on a shape characteristic of the Reeb graph.
摘要翻译：一种用于分析身体部位的医学图像中的感兴趣区域的形状的方法，包括：找到医学图像中的感兴趣区域; 计算感兴趣区域的Reeb图，并且基于Reeb图的形状特征来确定感兴趣区域是否是恶性病变候选。

26. 发明申请

US20100166276A1 PROBABILISTIC SEGMENTATION IN COMPUTER-AIDED DETECTION 有权
标题翻译：计算机辅助检测中的概率分类
公开(公告)号：US20100166276A1
公开(公告)日：2010-07-01
申请号：US12555799
申请日：2009-09-08
申请人： Shishir Dube , Gerardo Hermosillo Valadez , Sarang Lakare , Yoshihisa Shinagawa
发明人： Shishir Dube , Gerardo Hermosillo Valadez , Sarang Lakare , Yoshihisa Shinagawa
IPC分类号： G06K9/34 , G06K9/00
CPC分类号： G06K9/342 , G06T5/002 , G06T5/40 , G06T7/11 , G06T7/187 , G06T2207/30068
摘要： Described herein is a method and system for facilitating segmentation of images. A difference image is received and processed to extract at least one histogram (402). A noise component is determined by fitting a symmetric Gaussian distribution to the extracted histogram such that the negative portion of the Gaussian distribution coincides with the negative portion of the histogram (403). The noise component is then subtracted from the histogram to generate a probability distribution function (404), which may be converted to a cumulative distribution function (406) and applied to the difference image to generate a probabilistic representation of contrast enhancement (408).
摘要翻译：这里描述了一种便于图像分割的方法和系统。差分图像被接收和处理以提取至少一个直方图（402）。通过将对称高斯分布拟合到所提取的直方图来确定噪声分量，使得高斯分布的负部分与直方图（403）的负部分重合。然后从直方图中减去噪声分量以产生概率分布函数（404），该概率分布函数可被转换为累积分布函数（406）并应用于差分图像以生成对比度增强的概率表示（408）。

27. 发明申请

US20090069665A1 Automatic Lesion Correlation in Multiple MR Modalities 审中-公开
标题翻译：多种MR模式的自动病变相关性
公开(公告)号：US20090069665A1
公开(公告)日：2009-03-12
申请号：US12204882
申请日：2008-09-05
申请人： Gerardo Hermosillo Valadez , Yoshihisa Shinagawa
发明人： Gerardo Hermosillo Valadez , Yoshihisa Shinagawa
IPC分类号： A61B5/055
CPC分类号： G06T7/38
摘要： A method for automatic correlation between multiple magnetic resonance (MR) modalities includes acquiring first MR image data form a first modality. Second MR image data is acquired from a second modality. One or more anatomical landmarks are detected within both the first and second MR image data and the first and second MR image data are automatically correlated based on the detected anatomical landmarks and interpolation using a learning deformation function. The automatic correlation is refined using a local search.
摘要翻译：一种用于多个磁共振（MR）模态之间的自相关的方法包括从第一模态获取第一MR图像数据。从第二模式获取第二MR图像数据。在第一和第二MR图像数据中都检测到一个或多个解剖标志，并且基于检测到的解剖标志和使用学习变形函数进行内插，第一和第二MR图像数据被自动相关。使用本地搜索来改进自动关联。

28. 发明申请

US20090067698A1 Robust Segmentation of Breast and Muscle in MRI 有权
标题翻译：乳腺和肌肉在MRI中的稳健分割
公开(公告)号：US20090067698A1
公开(公告)日：2009-03-12
申请号：US12207826
申请日：2008-09-10
申请人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
发明人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
IPC分类号： G06K9/00
CPC分类号： G06T11/003 , G06T7/11 , G06T7/168 , G06T2207/10088 , G06T2207/30068 , G06T2207/30096
摘要： A method for segmenting an anatomical structure within medical image data includes acquiring medical image data. The medical image data is transformed from an original image space into a projective dual image space. A boundary of an anatomical structure is identified within the transformed medical image data based on a set of preexisting training data. An inverse transform is performed on the transformed medical image data and the identified boundary to convert the transformed medical image data and the identified boundary into the original image space. The inverse transformed identified boundary of the anatomical structure is used to segment the anatomical structure within the acquired medical image data.
摘要翻译：用于在医学图像数据内分割解剖结构的方法包括获取医学图像数据。医学图像数据从原始图像空间转换为投影双图像空间。基于一组先前存在的训练数据，在转化的医学图像数据内识别解剖结构的边界。对变换的医学图像数据和所识别的边界执行逆变换，以将转换的医学图像数据和所识别的边界转换为原始图像空间。解剖结构的逆变换识别边界用于分割所获取的医学图像数据内的解剖结构。

29. 发明申请

US20090067694A1 Efficient Features for Shape Analysis of Lesions in Breast MR 有权
标题翻译：乳腺MR病变形态分析的高效特征
公开(公告)号：US20090067694A1
公开(公告)日：2009-03-12
申请号：US12204953
申请日：2008-09-05
申请人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
发明人： Yoshihisa Shinagawa , Gerardo Hermosillo Valadez
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06K9/469 , G06T2207/10088 , G06T2207/20072 , G06T2207/30068
摘要： A method for analyzing a shape of a region of interest in a medical image of a body part, including: finding a region of interest in the medical image; calculating a Reeb graph of the region of interest, and determining whether the region of interest is a malignant lesion candidate based on a shape characteristic of the Reeb graph.
摘要翻译：一种用于分析身体部位的医学图像中的感兴趣区域的形状的方法，包括：找到医学图像中的感兴趣区域; 计算感兴趣区域的Reeb图，并且基于Reeb图的形状特征来确定感兴趣区域是否是恶性病变候选。

30. 发明申请

US20110064289A1 Systems and Methods for Multilevel Nodule Attachment Classification in 3D CT Lung Images 审中-公开
标题翻译： 3D CT肺图像多层结节附件分类系统与方法
公开(公告)号：US20110064289A1
公开(公告)日：2011-03-17
申请号：US12880385
申请日：2010-09-13
申请人： Jinbo Bi , Le Lu , Marcos Salganicoff , Yoshihisa Shinagawa , Dijia Wu
发明人： Jinbo Bi , Le Lu , Marcos Salganicoff , Yoshihisa Shinagawa , Dijia Wu
IPC分类号： G06K9/62
CPC分类号： G06T7/0012 , G06K2209/053 , G06T2207/30064
摘要： Automated and semi-automated systems and methods for detection and classification of structures within 3D lung CT images using voxel-level segmentation and subvolume-level classification.
摘要翻译：自动和半自动化系统和方法，用于使用体素级分割和子体积级别分类来检测和分类3D肺CT图像内的结构。

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