Image Segmentation Based on Local Chan-Vese Model Combined with Fractional Order Derivative

Image segmentation plays a significant role in computer vision and image processing. In this paper, we proposed a novel Local Chan–Vese (LCV) image segmentation model. The new model combined classical LCV model with fractional order magnitude image. We used absolute value instead of square root operation to approximate the magnitude of fractional order gradient, and constructed the eight directions \( 5 \times 5 \) fractional differential masks. We can get a novel fractional order difference image and drive a new local image fitting term. We also presented a new distance regularized term. The new distance regularization term was defined by a potential function. We used the spectral residual method for getting the saliency map of the given image. The initial level set function was driven based on saliency map to accelerate the convergence speed. The experiments were given to show the effectiveness of the new image segmentation model.

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