Machine learning for improved size estimation of complex marine particles from noisy holographic images
Size estimation of particles and plankton is key to understanding energy flows in the marine ecosystem. A useful tool to determine particle and plankton size - besides abundance and taxonomy - is in situ imaging, with digital holography being particularly useful for micro-scale (e.g., 25 – 2,500 µm)...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1587939/full |
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| Summary: | Size estimation of particles and plankton is key to understanding energy flows in the marine ecosystem. A useful tool to determine particle and plankton size - besides abundance and taxonomy - is in situ imaging, with digital holography being particularly useful for micro-scale (e.g., 25 – 2,500 µm) marine particles. However, most standard algorithms fail to accurately size objects in reconstructed holograms owing to the high background noise. Here we develop a machine-learning-based method for determining the size of natural objects recorded in digital holograms. A structured-forests-based edge detector is trained and refined for detecting the particle (soft) edges. A set of pixel-wise morphology operators are then used to extract particle regions (masks) from their edge images. Lastly, the size information of particles is calculated based on these extract masks. Our results show that the proposed strategy of training the model on synthetic and real holographic data improves the model’s performance on edge detection in holographic images. Compared with another ten methods, our method has the best performance and is capable of rapidly and accurately extracting particles’ regions on a group of synthetic and real holograms (natural oceanic particles), respectively (mean IoU: 0.81 and 0.76; standard-deviation IoU: 0.18 and 0.15). |
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| ISSN: | 2296-7745 |