학술논문
Detection of Steatosis Disease using Region Based Segmentation of Growcut Algorithm and Comparing with Region Growing Algorithm to Enhance the Accuracy
Document Type
Conference
Author
Source
2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) Science Technology Engineering and Mathematics (ICONSTEM), 2023 Eighth International Conference on. :1-6 Apr, 2023
Subject
Language
Abstract
To detect steatosis disease using region-based segmentation of growcut algorithm and comparing with the Region Growing Algorithm. Materials and Methods includes steatosis disease images, growcut algorithm and Region Growing Algorithm. The images were segmented using the growcut algorithm and the accuracy of the segmentation was compared with the Region Growing algorithm. The segmentation process was done by using MATLAB Software in the digital image processing lab and the total sample size for the 2 groups is 40 and the sample amount is 20. The results showed that the growcut algorithm was more accurate than the region growing algorithm for segmenting the steatosis disease images. The conclusion is that the growcut algorithm is a better method for segmenting the steatosis images than the Region Growing Algorithm. The accuracy of the growcut algorithm was higher than that of the Region Growing Algorithm. Thus, this is recommended as a better segmentation method for steatosis images.