# Adaptive Morphological Reconstruction for Seeded Image Segmentation

## TL;DR

This paper introduces an adaptive morphological reconstruction method that improves seeded image segmentation by effectively filtering seeds, being scale-insensitive, and enabling hierarchical segmentation, resulting in better accuracy and efficiency.

## Contribution

The paper proposes a novel adaptive morphological reconstruction technique that enhances seed filtering, scale insensitivity, and hierarchical segmentation in seeded image segmentation algorithms.

## Key findings

- AMR improves seed filtering accuracy.
- AMR enhances segmentation quality over state-of-the-art methods.
- AMR reduces computational time in segmentation tasks.

## Abstract

Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to filter seeds (regional minima) to reduce over-segmentation. However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. Firstly, AMR can adaptively filter useless seeds while preserving meaningful ones. Secondly, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that AMR is useful for improving algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time. Source code is available at https://github.com/SUST-reynole/AMR.