Abstract
Active contour model (ACM) has been widely used in image segmentation. The original ACM has poor weak edge preservation ability and it is difficult to converge to the concave, especially long and thin indentation convergence. In order to address these defects, a series of models such as gradient vector flow (GVF) and general gradient vector flow (GGVF) were proposed. A new edge-preserving ACM using oriented smoothness, infinite Laplacian is proposed in this paper to further address these issues. Oriented smoothness and infinite Laplacian are adopted as the smoothness term in the energy function to promote the model’s weak edge preservation and concave convergence ability. Furthermore, we employ a component-based normalization to accelerate the concave convergence rate. The experimental results show that the proposed method achieves better performance than the other comparative methods.
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This work was supported by the NSFC under Grants 61401386, 61802328.
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Cao, C., Zhou, C., Yu, J., Hu, K., Xiao, F. (2020). A Novel Active Contour Model Using Oriented Smoothness and Infinite Laplacian for Medical Image Segmentation. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_31
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