Abstract
Image segmentation is regarded as the most crucial medical imaging process because it extracts the region of interest and thus helps upgrade medical diagnosis. The Geodesic deformable model is a curve that deforms within digital images to extract object shapes. The Geodesic deformable model has been used very successfully in the process of single image segmentation but it fails to segment a series of images. In this research, the Geodesic deformable model is developed to overcome its limitation by controlling the speed of the curve deformation. The developed model is implemented on several series of MRI images that include tumors of varying shape complexity. Experimental results show that the developed model performed very well and successfully segmented the series of MRI images which outperform the baseline model.
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