Fractional Differentiation-based Hybrid Active Contour Model for Noisy Image Segmentation
How to Cite?
Srikanth Khanna, Venkatachalam Chandrasekaran, "Fractional Differentiation-based Hybrid Active Contour Model for Noisy Image Segmentation," International Journal of Engineering Trends and Technology, vol. 69, no. 8, pp. 243-259, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I8P230
Image segmentation in the presence of noise is a challenging task. Performance of the methods which are efficient in noiseless images degrades in the presence of noise. In this paper, we propose a novel fractional derivative-based hybrid active contour for robust noisy image segmentation. By incorporating a novel fractional derivative-based balloon term and a fractional derivative-based edge term along with a region scalable fitting function, we obtain a method which provides good segmentation performance even in high noise scenarios without any change in the method parameters. We demonstrate that the proposed method outperforms the conventional methods in the presence of Gaussian, speckle and bipolar noises.
active contour, fractional derivative, level set, noisy image segmentation
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