  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.
