Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information

Author (s): Giacomini, M. and Perotto, S.
Journal: Computers and Mathematics with Applications

Volume: 121
Pages: 1 – 17
Date: 2022

Abstract:
A finite element-based image segmentation strategy enhanced by an anisotropic mesh adaptation procedure is presented. The methodology relies on a split Bregman algorithm for the minimisation of a region-based energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation. More precisely, a Bayesian energy functional is considered to account for image spatial information, ensuring that the methodology is able to identify inhomogeneous spatial patterns in complex images. In addition, the anisotropic mesh adaptation guarantees a sharp detection of the interface between background and foreground of the image, with a reduced number of degrees of freedom. The resulting split-adapt Bregman algorithm is tested on a set of real images showing the accuracy and robustness of the method, even in the presence of Gaussian, salt and pepper and speckle noise.

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Bibtex:

@article{MG-GP-22,
        author = {Matteo Giacomini and Simona Perotto},
        title = {Anisotropic mesh adaptation for region-based segmentation accounting 
                 for image spatial information},
        fjournal = {Computers \& Mathematics with Applications},
        journal = {Comput. Math. with Appl.},
        volume = {121},
        pages = {1--17},
        year = {2022},
        doi = {10.1016/j.camwa.2022.06.025},
}