A Novel Approach to Segmenting Images of Paralysis-Affected Individuals Using GrabCut

Author(s)	Ms. Shireen Kausar, Prof. Dr. Suvarna Nandyal
Country	India
Abstract	Using a dataset of patient visuals with paralysis to isolate and highlight areas that are impaired using the GrabCut image segmentation technique. With little assistance from the user, GrabCut, a semi-automatic segmentation technique based on Gaussian Mixture Models (GMMs) and repeated graph cuts, is quite effective in removing foreground objects from complicated backgrounds. A individual dataset made up of images of the hands and leg of patients with partial paralysis was used to test the recommended method. GrabCut performs better than conventional methods in terms of accuracy and visual quality when compared to common segmentation algorithms like K-means clustering and Otsu's thresholding.
Keywords	GrabCut, Image Segmentation, Paralysis, Medical Imaging, Gaussian Mixture Model, Foreground Extraction, Semi-Automatic Segmentation
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-11
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.50472
Short DOI	https://doi.org/g9s9jm

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