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21 条记录 (耗时 0.014 秒)

    Multi-bone segmentation for 3D computed tomography
    1.
    发明授权
    Multi-bone segmentation for 3D computed tomography 有权
    用于3D计算机断层扫描的多骨分割

    公开(公告)号：US09495752B2

    公开(公告)日：2016-11-15

    申请号：US13953825

    申请日：2013-07-30

    申请人： Dijia Wu ,  Neil Birkbeck ,  Michal Sofka ,  Meizhu Liu ,  Shaohua Kevin Zhou

    发明人： Dijia Wu ,  Neil Birkbeck ,  Michal Sofka ,  Meizhu Liu ,  Shaohua Kevin Zhou

    IPC分类号： G06T7/00

    CPC分类号： G06T7/0079 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/143 ,  G06T2207/10072 ,  G06T2207/20041 ,  G06T2207/20076

    摘要： Multiple object segmentation is performed for three-dimensional computed tomography. The adjacent objects are individually segmented. Overlapping regions or locations designated as belonging to both objects may be identified. Confidence maps for the individual segmentations are used to label the locations of the overlap as belonging to one or the other object, not both. This re-segmentation is applied for the overlapping local, and not other locations. Confidence maps in re-segmentation and application just to overlap locations may be used independently of each other or in combination.

    摘要翻译： 对三维计算机断层摄影进行多目标分割。 相邻的对象被单独分段。 可以识别指定为属于两个对象的重叠区域或位置。 用于各个分段的置信图用于将重叠的位置标记为属于一个或另一个对象，而不是两者。 这种重新分割被应用于重叠的本地，而不是其他位置。 重新分割和应用恰好重叠位置的置信度图可以彼此独立地或组合地使用。

    AUTOMATIC SPATIAL CONTEXT BASED MULTI-OBJECT SEGMENTATION IN 3D IMAGES
    2.
    发明申请
    AUTOMATIC SPATIAL CONTEXT BASED MULTI-OBJECT SEGMENTATION IN 3D IMAGES 有权
    基于自动空间语境的3D图像多目标分割

    公开(公告)号：US20140161334A1

    公开(公告)日：2014-06-12

    申请号：US13776184

    申请日：2013-02-25

    申请人： Quan Wang ,  Dijia Wu ,  Meizhu Liu ,  Le Lu ,  Kevin Shaohua Zhou

    发明人： Quan Wang ,  Dijia Wu ,  Meizhu Liu ,  Le Lu ,  Kevin Shaohua Zhou

    IPC分类号： G06K9/00

    CPC分类号： G06K9/00362 ,  G06K9/6219 ,  G06K9/6282 ,  G06K2209/055 ,  G06T7/12 ,  G06T7/149 ,  G06T2207/10088 ,  G06T2207/30008

    摘要： Methods and systems for automatic classification of images of internal structures of human and animal bodies. A method includes receiving a magnetic resonance (MR) image testing model and determining a testing volume of the testing model that includes areas of the testing model to be classified as bone or cartilage. The method includes modifying the testing model so that the testing volume corresponds to a mean shape and a shape variation space of an active shape model and producing an initial classification of the testing volume by fitting the testing volume to the mean shape and the shape variation space. The method includes producing a refined classification of the testing volume into bone areas and cartilage areas by refining the boundaries of the testing volume with respect to the active shape model and segmenting the MR image testing model into different areas corresponding to bone areas and cartilage areas.

    摘要翻译： 自动分类人体和动物体内部结构图像的方法和系统。 一种方法包括接收磁共振（MR）图像测试模型并确定包括要分类为骨或软骨的测试模型的区域的测试模型的测试体积。 该方法包括修改测试模型，使得测试体积对应于活动形状模型的平均形状和形状变化空间，并通过将测试体积与平均形状和形状变化空间拟合来产生测试体积的初始分类 。 该方法包括通过相对于活性形状模型细化测试体积的边界并将MR图像测试模型分割成对应于骨区域和软骨区域的不同区域，来将测试体积的精细分类生成到骨区域和软骨区域中。

    Automatic spatial context based multi-object segmentation in 3D images
    3.
    发明授权
    Automatic spatial context based multi-object segmentation in 3D images 有权
    在3D图像中基于自动空间上下文的多对象分割

    公开(公告)号：US09218524B2

    公开(公告)日：2015-12-22

    申请号：US13776184

    申请日：2013-02-25

    申请人： Quan Wang ,  Dijia Wu ,  Meizhu Liu ,  Le Lu ,  Kevin Shaohua Zhou

    发明人： Quan Wang ,  Dijia Wu ,  Meizhu Liu ,  Le Lu ,  Kevin Shaohua Zhou

    IPC分类号： G06K9/00 ,  G06T7/00 ,  G06K9/62

    CPC分类号： G06K9/00362 ,  G06K9/6219 ,  G06K9/6282 ,  G06K2209/055 ,  G06T7/12 ,  G06T7/149 ,  G06T2207/10088 ,  G06T2207/30008

    摘要： Methods and systems for automatic classification of images of internal structures of human and animal bodies. A method includes receiving a magnetic resonance (MR) image testing model and determining a testing volume of the testing model that includes areas of the testing model to be classified as bone or cartilage. The method includes modifying the testing model so that the testing volume corresponds to a mean shape and a shape variation space of an active shape model and producing an initial classification of the testing volume by fitting the testing volume to the mean shape and the shape variation space. The method includes producing a refined classification of the testing volume into bone areas and cartilage areas by refining the boundaries of the testing volume with respect to the active shape model and segmenting the MR image testing model into different areas corresponding to bone areas and cartilage areas.

    摘要翻译： 自动分类人体和动物体内部结构图像的方法和系统。 一种方法包括接收磁共振（MR）图像测试模型并确定包括要分类为骨或软骨的测试模型的区域的测试模型的测试体积。 该方法包括修改测试模型，使得测试体积对应于活动形状模型的平均形状和形状变化空间，并通过将测试体积与平均形状和形状变化空间拟合来产生测试体积的初始分类 。 该方法包括通过相对于活性形状模型细化测试体积的边界并将MR图像测试模型分割成对应于骨区域和软骨区域的不同区域，来将测试体积的精细分类生成到骨区域和软骨区域中。

    Interleaved video frame buffer structure
    6.
    发明申请
    Interleaved video frame buffer structure 审中-公开
    交错视频帧缓冲结构

    公开(公告)号：US20070126747A1

    公开(公告)日：2007-06-07

    申请号：US11292985

    申请日：2005-12-02

    申请人： Dijia Wu ,  Fan Zhang

    发明人： Dijia Wu ,  Fan Zhang

    IPC分类号： G09G5/36

    CPC分类号： G09G5/022 ,  G09G5/393 ,  G09G2340/02 ,  G09G2360/121 ,  G09G2360/123 ,  H04N19/127 ,  H04N19/176 ,  H04N19/186 ,  H04N19/423 ,  H04N19/61

    摘要： An embodiment is an interleaved video frame buffer structure that merges two separate chroma frame buffers into one chroma frame buffer by interleaving the individual chroma frame buffers. The merged chroma buffer, based on improved memory space adjacency versus two separate chroma frame buffers, may reduce the possibility of cache conflicts and improve cache utilization. Other embodiments are described and claimed.

    摘要翻译： 一个实施例是交织的视频帧缓冲器结构，其通过交织各个色度帧缓冲器将两个单独的色度帧缓冲器合并成一个色度帧缓冲器。 基于改进的存储器空间相邻性与两个单独的色度帧缓冲器的合并色度缓冲器可以减少高速缓存冲突的可能性并提高高速缓存利用率。 描述和要求保护其他实施例。

    Multi-level contextual learning of data
    7.
    发明授权
    Multi-level contextual learning of data 有权
    数据的多层次上下文学习

    公开(公告)号：US08724866B2

    公开(公告)日：2014-05-13

    申请号：US12962901

    申请日：2010-12-08

    申请人： Dijia Wu ,  Le Lu ,  Jinbo Bi ,  Yoshihisa Shinagawa ,  Marcos Salganicoff

    发明人： Dijia Wu ,  Le Lu ,  Jinbo Bi ,  Yoshihisa Shinagawa ,  Marcos Salganicoff

    IPC分类号： G06K9/00

    CPC分类号： G06K9/4638 ,  G06K2209/05

    摘要： Described herein is a framework for automatically classifying a structure in digital image data are described herein. In one implementation, a first set of features is extracted from digital image data, and used to learn a discriminative model. The discriminative model may be associated with at least one conditional probability of a class label given an image data observation Based on the conditional probability, at least one likelihood measure of the structure co-occurring with another structure in the same sub-volume of the digital image data is determined. A second set of features may then be extracted from the likelihood measure.

    摘要翻译： 这里描述了用于自动分类数字图像数据中的结构的框架。 在一个实现中，从数字图像数据中提取第一组特征，并用于学习辨别模型。 鉴别模型可以与给定图像数据观察的类标签的至少一个条件概率相关联。基于条件概率，与数字的相同子体积中的另一结构共同出现的结构的至少一个似然度量 确定图像数据。 然后可以从似然度量中提取第二组特征。