A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes

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 ×1.5 up to ×3.1 for usual medical datasets and random images.

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

Maurice

Lemaître

Julien

et al. 2022

Lecture Notes in Computer Science

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A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes

Abstract: This paper provides a detailed description of how to install, setup, and use the YACCLAB benchmark to test the algorithms published in "A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes," underlying how the parameters affect and influence experimental results.

Cited by 1 publication

References 33 publications

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

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

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