# A High-Order Scheme for Image Segmentation via a modified Level-Set method

## TL;DR

This paper introduces a novel high-order level-set scheme for image segmentation that enhances stability and accuracy by modifying the velocity function and employing an adaptive filtered scheme, validated through synthetic and real image tests.

## Contribution

It presents the first 2D adaptive filtered scheme for level-set image segmentation, combining high-order accuracy with stability and efficiency.

## Key findings

- Improved segmentation accuracy on synthetic and real images.
- Enhanced stability of the level-set evolution.
- Efficient implementation of the high-order scheme.

## Abstract

In this paper we propose a high-order accurate scheme for image segmentation based on the level-set method. In this approach, the curve evolution is described as the 0-level set of a representation function but we modify the velocity that drives the curve to the boundary of the object in order to obtain a new velocity with additional properties that are extremely useful to develop a more stable high-order approximation with a small additional cost. The approximation scheme proposed here is the first 2D version of an adaptive "filtered" scheme recently introduced and analyzed by the authors in 1D. This approach is interesting since the implementation of the filtered scheme is rather efficient and easy. The scheme combines two building blocks (a monotone scheme and a high-order scheme) via a filter function and smoothness indicators that allow to detect the regularity of the approximate solution adapting the scheme in an automatic way. Some numerical tests on synthetic and real images confirm the accuracy of the proposed method and the advantages given by the new velocity.