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

TL;DR

This paper introduces an anisotropic mesh adaptation technique combined with a split Bregman algorithm for efficient, accurate region-based image segmentation that effectively handles complex images with noise.

Contribution

It presents a novel finite element-based segmentation method that integrates anisotropic mesh adaptation with a Bayesian energy functional to improve interface detection.

Findings

Accurately detects image interfaces with fewer degrees of freedom.

Robust against various types of noise such as Gaussian, salt-and-pepper, and speckle.

Demonstrates effectiveness on real noisy images.

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.