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

arXiv: https://arxiv.org/abs/2112.10138 · HTML: https://mlsanity.com/paper/2112.10138/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 89 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study](https://mlsanity.com/paper/1612.03925/) (2016)
- [A High-Order Scheme for Image Segmentation via a modified Level-Set method](https://mlsanity.com/paper/1812.03026/) (2018)
- [Certified Descent Algorithm for shape optimization driven by fully-computable a posteriori error estimators](https://mlsanity.com/paper/1604.04921/) (2016)

## Abstract (arXiv)

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.