US 11,866,791 B2
Identifying candidate cells using image analysis with intensity levels
Huangpin B. Hsieh, Palo Alto, CA (US); XiaoMing Wang, Dublin, CA (US); Jr-Ming Lai, Taipei (TW); Rui Mei, Santa Clara, CA (US); Hung-Jen Shao, Taipei (TW); and Jen-Chia Wu, Taipei (TW)
Assigned to CellMax Ltd., Sunnyvale, CA (US)
Filed by CellMax Ltd., Grand Cayman (KY)
Filed on Sep. 8, 2022, as Appl. No. 17/940,582.
Application 17/940,582 is a division of application No. 16/740,799, filed on Jan. 13, 2020.
Application 16/740,799 is a continuation of application No. 15/998,990, filed on Aug. 20, 2018, granted, now 10,533,230, issued on Jan. 14, 2020.
Application 15/998,990 is a continuation of application No. 15/632,707, filed on Jun. 26, 2017, granted, now 10,053,739, issued on Aug. 21, 2018.
Application 15/632,707 is a continuation of application No. 15/476,848, filed on Mar. 31, 2017, granted, now 9,738,937, issued on Aug. 22, 2017.
Prior Publication US 2023/0002834 A1, Jan. 5, 2023
Int. Cl. G06K 9/00 (2022.01); C12Q 1/6886 (2018.01); G01N 15/14 (2006.01); G01N 33/574 (2006.01); G01N 33/50 (2006.01); G01N 1/31 (2006.01); G01N 33/58 (2006.01); C12Q 1/6883 (2018.01); G06T 7/00 (2017.01)
CPC C12Q 1/6886 (2013.01) [G01N 1/31 (2013.01); G01N 15/1475 (2013.01); G01N 33/5094 (2013.01); G01N 33/57484 (2013.01); G01N 33/582 (2013.01); C12Q 1/6883 (2013.01); G01N 15/1468 (2013.01); G01N 2015/1472 (2013.01); G01N 2333/4742 (2013.01); G01N 2333/70589 (2013.01); G06T 7/0012 (2013.01); G06T 2207/10056 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30101 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A computer program product for identifying candidate target cells within a biological fluid specimen, the computer program product tangibly embodied in a non-transitory computer readable medium, comprising instructions to cause a processor to:
receive a digital image of the biological fluid specimen;
identify one or more candidate regions of pixels in the digital image, wherein the instructions to identify the one or more candidate regions include instructions to identify 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; and
for each candidate region of at least one of the one or more candidate region
analyze the image to identify distinct gray levels in the candidate region,
performing a count of the gray levels, and
determine whether the count of gray levels is more than a threshold number, and if the count of gray levels is more than the threshold number then continue to treat the portion of the image as a candidate for classification.