Detection of Steatosis Disease using Region Based Segmentation of Growcut Algorithm and Comparing with Random Walker Algorithm to Enhance the Accuracy

To detect steatosis disease using region-based segmentation of GrowCut Algorithm and comparing with Random Walker Algorithm. Materials and Methods: It includes steatosis disease images, GrowCut algorithm and Random Walker Algorithm. The images were segmented using the GrowCut Algorithm and the accuracy of the segmentation was compared with the Random Walker algorithm. In the digital image processing lab, the segmentation procedure was carried out using MATLAB software, with a total sample size of 40 for the two groups and a sample quantity of 20. Results: It showed that the GrowCut Algorithm was more accurate than the Random Walker Algorithm for segmenting the steatosis disease images. Conclusion: The GrowCut Algorithm is a better method for segmenting the steatosis images than the Random Walker Algorithm. The GrowCut algorithm was more accurate than the Random Walker algorithm. As a result, the GrowCut Algorithm is suggested as a superior segmentation technique for pictures with steatosis.