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    Detection of structure in ultrasound M-mode imaging
    1.
    发明授权
    Detection of structure in ultrasound M-mode imaging 有权
    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。

    Detection of Structure in Ultrasound M-Mode Imaging
    2.
    发明申请
    Detection of Structure in Ultrasound M-Mode Imaging 有权
    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。

    Hierarchical alignment of character sequences representing text of same source
    7.
    发明授权
    Hierarchical alignment of character sequences representing text of same source 有权
    表示相同来源的文本的字符序列的分层对齐

    公开(公告)号：US08170289B1

    公开(公告)日：2012-05-01

    申请号：US11232476

    申请日：2005-09-21

    申请人： Shaolei Feng ,  Raghavan Manmatha

    发明人： Shaolei Feng ,  Raghavan Manmatha

    IPC分类号： G06K9/00

    CPC分类号： G06F17/2211

    摘要： Systems and methods for character-by-character alignment of two character sequences (such as OCR output from a scanned document and an electronic version of the same document) using a Hidden Markov Model (HMM) in a hierarchical fashion are disclosed. The method may include aligning two character sequences utilizing multiple hierarchical levels. For each hierarchical level above a final hierarchical level, the aligning may include parsing character subsequences from the two character sequences, performing an alignment of the character subsequences, and designating aligned character subsequences as the anchors, the parsing and performing the alignment being between the anchors generated from an immediately previous hierarchical level if the current hierarchical level is below the first hierarchical level. For the final hierarchical level, the aligning includes performing a character-by-character alignment of characters between anchors generated from the immediately previous hierarchical level. At each hierarchical level, an HMM may be constructed and Viterbi algorithm may be employed to solve for the alignment.

    摘要翻译： 公开了使用隐马尔可夫模型（HMM）以分级方式对两个字符序列（例如来自扫描文档的OCR输出和相同文档的电子版本）进行逐字符对准的系统和方法。 该方法可以包括利用多个层级对准两个字符序列。 对于最终层次级别以上的每个层级，对齐可以包括从两个字符序列解析字符子序列，执行字符子序列的对齐，并且指定对齐的字符子序列作为锚点，解析并执行对齐在锚点之间 如果当前分层级别低于第一层次级别，则从紧接在前的分级级别生成。 对于最终层次级别，对齐方式包括执行从紧接在之前的层次级别生成的锚点之间的字符逐字符对齐。 在每个层级上，可以构造HMM，并且可以采用维特比算法来解决对齐。