The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 09, 2025

Filed:

Dec. 04, 2020
Applicant:

Osaka University, Osaka, JP;

Inventors:

Hodaka Numasaki, Osaka, JP;

Shoichi Tomohisa, Osaka, JP;

Masahiko Koizumi, Osaka, JP;

Hirofumi Yamamoto, Osaka, JP;

Kazuaki Nakane, Osaka, JP;

Noriyuki Tomiyama, Osaka, JP;

Masahiro Yanagawa, Osaka, JP;

Osamu Honda, Osaka, JP;

Assignee:

Osaka University, Osaka, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/62 (2022.01); A61B 6/03 (2006.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06V 10/25 (2022.01); G06V 10/75 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G06T 7/11 (2017.01); G06T 2207/30024 (2013.01);
Abstract

The disclosed feature makes it possible to accurately determine a change that has occurred in a tissue. The feature includes: a binarizing section () that generates, from an image to be analyzed, a plurality of binarized images having respective binarization reference values different from each other; a Betti number calculating section () that calculates, for each of the plurality of binarized images, a one-dimensional Betti number indicating the number of hole-shaped regions each of which is surrounded by pixels each having a first pixel value obtained by binarization and is constituted by pixels each having a second pixel value obtained by binarization; and a determining section () that determines a change that has occurred in the tissue, based on a binarization reference value and a one-dimensional Betti number in a binarized image in which the one-dimensional Betti number is maximized.


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