Seeded Segmentation

Seeded Segmentation is a volume segmentation method that incorporates user-defined or algorithmically generated initial markers, or "seeds," to guide the delineation of regions through region-growing, graph-based, or probabilistic inference techniques, thereby distinguishing it from unseeded or fully automated segmentation approaches.

95

Publications

9.7K

Citations

316

Authors

146

Institutions

Publications per year

2017–2026

14

Variants

Seed-based Segmentation

Authors

316

  1. PA
    Paulo A. V. Miranda

    Universidade Estadual de Campinas (UNICAMP), Brazil

    2 publications · 27 citations

    2

    h-index

  2. NE
    Noor Elaiza Abdul Khalid

    Universiti Teknologi MARA, Malaysia

    2 publications · 51 citations

    2

    h-index

  3. DL
    Dac‐Nhuong Le

    Duy Tan University, Vietnam

    2 publications · 184 citations

    2

    h-index

  4. AS
    Amira S. Ashour

    Tanta University, Egypt

    2 publications · 184 citations

    2

    h-index

  5. CK
    Chung-Chia Kang

    National Central University, Taiwan

    2 publications · 116 citations

    2

    h-index

1–5 of 316

Venues

  1. 5

    h-index

  2. IEEE Transactions on Image Processing

    3 publications · 868 citations

    3

    h-index

  3. 3

    h-index

  4. Pattern Recognition Letters

    2 publications · 592 citations

    2

    h-index

  5. 2

    h-index

1–5