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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Imaging

    Background:

    • Morphological reconstruction (MR) is used in seeded image segmentation to reduce over-segmentation by filtering regional minima.
    • Standard MR can incorrectly remove essential seeds and is sensitive to scale due to single-scale structuring elements.

    Purpose of the Study:

    • To propose a novel adaptive morphological reconstruction (AMR) operation for seeded image segmentation.
    • To address limitations of standard MR, including seed filtering accuracy and scale sensitivity.

    Main Methods:

    • Developed an adaptive morphological reconstruction (AMR) operation employing multiscale structuring elements.
    • AMR adaptively filters seeds, preserving meaningful ones while removing irrelevant ones.
    • Incorporated monotonic increasingness and convergence properties for hierarchical segmentation.

    Main Results:

    • AMR demonstrated improved performance in seeded image segmentation and seed-based spectral segmentation.
    • The proposed AMR method is insensitive to the scale of structuring elements.
    • AMR achieved better segmentation results with reduced computing time compared to state-of-the-art algorithms.

    Conclusions:

    • Adaptive morphological reconstruction (AMR) enhances seeded image segmentation accuracy and efficiency.
    • AMR offers a robust solution for seed filtering and scale-insensitivity in image segmentation.
    • The method facilitates hierarchical segmentation and outperforms existing techniques.