Fast global region based minimization of satellite and medical imagery with geometric active contour and level set evolution on noisy images

Abstract

In this paper, we proposed a novel global region based segmentation method for satellite and medical images with geometric active contour model and level set evolution on noisy images with salt and pepper. The active contour or snake model is one of the most successful variational models in image segmentation. It has been widely used to locate boundaries of image segmentation and computer vision. Problem associated with the existence of the local minima in the active contour energy function makes snakes have poor convergence in segmentation process; therefore, the poor convergence has limited applications. In this work, a fast minimization of snake model is used for satellite and medical image segmentation on noisy images with ten percentage of Noisy was added. This method provides a satisfied result. As a result, it is a good candidate for medical image segmentation approach. Experiments on satellite images with noise demonstrate the advantages of the proposed method over the Chan-Vase (CV) active contour in terms of the number of Iterations and time complexity are less because it uses isotropic schemes to regularize the contour and is sub-pixel precise. Finally, the Memory requirement is low.