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    IDENTIFYING CANDIDATE CELLS USING IMAGE ANALYSIS WITH OVERLAP THRESHOLDS
    5.
    发明申请
    IDENTIFYING CANDIDATE CELLS USING IMAGE ANALYSIS WITH OVERLAP THRESHOLDS 有权

    公开(公告)号：US20230002835A1

    公开(公告)日：2023-01-05

    申请号：US17940625

    申请日：2022-09-08

    Applicant: CellMax Ltd.

    Inventor： Huangpin B. Hsieh ,  XiaoMing Wang ,  Jr-Ming Lai ,  Rui Mei ,  Hung-Jen Shao ,  Jen-Chia Wu

    IPC: C12Q1/6886 ,  G01N15/14 ,  G01N33/574 ,  G01N33/50 ,  G01N1/31 ,  G01N33/58

    Abstract: A method for identifying candidate target cells within a biological fluid specimen includes a digital image of the biological fluid specimen with the digital image having a plurality of color channels, identifying first connected regions of pixels of a minimum first intensity in a first channel, identifying second connected regions of pixels of a minimum second intensity in a second channel, and determining first connected regions and second connected regions that spatially overlap. For a pair of a first connected region and a second connected region that spatially overlap, whether the second connected region overlaps the first connected region by a threshold amount is determined, and if the second connected region overlaps the first connected region by the threshold amount then the portion of the image corresponding to the overlap is continued to be treated as a candidate for classification.

    IDENTIFYING CANDIDATE CELLS USING IMAGE ANALYSIS WITH OVERLAP THRESHOLDS
    7.
    发明申请
    IDENTIFYING CANDIDATE CELLS USING IMAGE ANALYSIS WITH OVERLAP THRESHOLDS 有权

    公开(公告)号：US20240392380A1

    公开(公告)日：2024-11-28

    申请号：US18391955

    申请日：2023-12-21

    Applicant: CellMax Ltd.

    Inventor： Huangpin B. Hsieh ,  XiaoMing Wang ,  Jr-Ming Lai ,  Rui Mei ,  Hung-Jen Shao ,  Jen-Chia Wu

    IPC: C12Q1/6886 ,  C12Q1/6883 ,  G01N1/31 ,  G01N15/14 ,  G01N15/1433 ,  G01N33/50 ,  G01N33/574 ,  G01N33/58 ,  G06T7/00

    Abstract: A method for identifying candidate target cells within a biological fluid specimen includes a digital image of the biological fluid specimen with the digital image having a plurality of color channels, identifying first connected regions of pixels of a minimum first intensity in a first channel, identifying second connected regions of pixels of a minimum second intensity in a second channel, and determining first connected regions and second connected regions that spatially overlap. For a pair of a first connected region and a second connected region that spatially overlap, whether the second connected region overlaps the first connected region by a threshold amount is determined, and if the second connected region overlaps the first connected region by the threshold amount then the portion of the image corresponding to the overlap is continued to be treated as a candidate for classification.

    IDENTIFYING CANDIDATE CELLS USING IMAGE ANALYSIS WITH INTENSITY LEVELS
    8.
    发明申请
    IDENTIFYING CANDIDATE CELLS USING IMAGE ANALYSIS WITH INTENSITY LEVELS 有权

    公开(公告)号：US20230002834A1

    公开(公告)日：2023-01-05

    申请号：US17940582

    申请日：2022-09-08

    Applicant: CellMax Ltd.

    Inventor： Huangpin B. Hsieh ,  XiaoMing Wang ,  Jr-Ming Lai ,  Rui Mei ,  Hung-Jen Shao ,  Jen-Chia Wu

    IPC: C12Q1/6886 ,  G01N15/14 ,  G01N33/574 ,  G01N33/50 ,  G01N1/31 ,  G01N33/58

    Abstract: Techniques for identifying and enumerating candidate target cells within a biological fluid specimen are described. A digital image of the biological fluid specimen is received, and one or more candidate regions of pixels in the digital image are identified by identifying connected regions of pixels of a minimum intensity having a size between a minimum size and a maximum size and an aspect ratio that meets a threshold. For each candidate region of at least one of the one or more candidate region, whether the portion of the image corresponding to the candidate region includes more than a threshold number of intensity levels is determined. If the portion of the image corresponding to the candidate region includes more than the threshold number of intensity levels the portion of the image is continued to be treated as a candidate for classification.

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