# Prior-based Hierarchical Segmentation Highlighting Structures of Interest

## TL;DR

This paper introduces a versatile hierarchical image segmentation method that incorporates prior spatial information to emphasize structures of interest across multiple scales, improving segmentation relevance.

## Contribution

It presents a novel hierarchical segmentation approach that effectively integrates prior spatial data to highlight important image structures at various levels.

## Key findings

- Method effectively emphasizes structures of interest
- Versatile application across different image types
- Demonstrates improved segmentation accuracy

## Abstract

Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.