US 11,657,499 B2
Method and apparatus for predicting pulmonary disease using fractal dimension value
Namkug Kim, Seoul (KR); Jeongeun Hwang, Seoul (KR); Joon Beom Seo, Seoul (KR); and Sang Min Lee, Seoul (KR)
Assigned to THE ASAN FOUNDATION, Seoul (KR); and UNIVERSITY OF ULSAN FOUNDATION FOR INDUSTRY COOPERATION, Ulsan (KR)
Filed by THE ASAN FOUNDATION, Seoul (KR); and UNIVERSITY OF ULSAN FOUNDATION FOR INDUSTRY COOPERATION, Ulsan (KR)
Filed on Jul. 21, 2020, as Appl. No. 16/934,103.
Application 16/934,103 is a continuation of application No. PCT/KR2019/000921, filed on Jan. 22, 2019.
Claims priority of application No. 10-2018-0007721 (KR), filed on Jan. 22, 2018.
Prior Publication US 2020/0349702 A1, Nov. 5, 2020
Int. Cl. G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G16H 30/20 (2018.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01)
CPC G06T 7/0012 (2013.01) [A61B 6/032 (2013.01); A61B 6/50 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G16H 30/20 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30061 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A method for predicting a pulmonary disease, the method comprising:
acquiring, by an image matching processor of a pulmonary disease prediction apparatus, a three-dimensional computed tomography (CT) image from two-dimensional CT images, each of which captures a respective body position of a first patient;
dividing, by a controller of the pulmonary disease prediction apparatus, the three-dimensional CT image into a plurality of regions, to generate region-based three-dimensional CT images configured to be used for fractal analysis;
calculating, by the controller, a region-based fractal dimension value indicating a fractal complexity of a respective region-based three-dimensional CT image among the generated region-based three-dimensional CT images;
adding, by the controller, additional information to the calculated region-based fractal dimension value to generate high-dimensional data; and
generating, by the controller, status information of the first patient based on a complexity of the generated high-dimensional data, wherein the first patient's state indicated by the generated status information depends on the complexity of the generated high-dimensional data and is differentiated according to a type of the disease to be predicted.