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International Conference on Image Analysis and Processing

ICIAP 2022: Image Analysis and Processing. ICIAP 2022 Workshops pp 132–142Cite as

LSL3D: A Run-Based Connected Component Labeling Algorithm for 3D Volumes

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13374))

Abstract

Connect Component Labeling (CCL) has been a fundamental operation in Computer Vision for decades. Most of the literature deals with 2D algorithms for applications like video surveillance or autonomous driving. Nonetheless, the need for 3D algorithms is rising, notably for medical imaging.

While 2D CCL algorithms already generate large amounts of memory accesses and comparisons, 3D ones are even worse. This is the curse of dimensionality. Designing an efficient algorithm should address this problem. This paper introduces a segment-based algorithm for 3D labeling that uses a new strategy to accelerate label equivalence processing to mitigate the impact of higher dimensions. We claim that this new algorithm outperforms State-of-the-Art algorithms by a factor from \(\times \)1.5 up to \(\times \)3.1 for usual medical datasets and random images.

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Author information

Authors and Affiliations

  1. LIP6, Sorbonne University, CNRS, Paris, France

    Nathan Maurice, Florian Lemaitre, Julien Sopena & Lionel Lacassagne

Authors
  1. Nathan Maurice
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  2. Florian Lemaitre
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  3. Julien Sopena
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  4. Lionel Lacassagne
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Corresponding author

Correspondence to Nathan Maurice .

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Editors and Affiliations

  1. National Research Council, Lecce, Italy

    Pier Luigi Mazzeo

  2. Università Politecnica delle Marche, Ancona, Italy

    Emanuele Frontoni

  3. Boston University, Boston, MA, USA

    Stan Sclaroff

  4. National Research Council, Lecce, Italy

    Cosimo Distante

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Maurice, N., Lemaitre, F., Sopena, J., Lacassagne, L. (2022). LSL3D: A Run-Based Connected Component Labeling Algorithm for 3D Volumes. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13374. Springer, Cham. https://doi.org/10.1007/978-3-031-13324-4_12

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  • DOI: https://doi.org/10.1007/978-3-031-13324-4_12

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  • Print ISBN: 978-3-031-13323-7

  • Online ISBN: 978-3-031-13324-4

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