Adaptive Morphological Reconstruction for Seeded Image Segmentation

Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 28; no. 11; pp. 5510 - 5523
Main Authors: Tao Lei, Xiaohong Jia, Tongliang Liu, Shigang Liu, Hongying Meng, Nandi, Asoke K.
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer science
Computing time
Convergence
Image edge detection
Image reconstruction
Image segmentation
Mathematical morphology
Morphology
seeded segmentation
Seeds
spectral segmentation
Transforms
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the 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. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of 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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2019.2920514