Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital In-Line Holography

Abstract

Substantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing, and classification of particles. These computational challenges are the subject of this paper, in which the authors draw upon data collected using a variety of holographic systems developed at Plymouth University, United Kingdom, from a significant range of particle types, sizes, and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from oversegmentation of particles. A simple unsupervised particle classification system is developed and is capable of successfully differentiating sand grains, bubbles, and diatoms from within the surfzone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments, and oil droplets.

Geometry, fractal dimension and settling velocity of flocs during flooding conditions in the Rhône ROFI

“…Floc selection criteria -The aspect ratio parameter enables the discrimination of some populations of SPM (Davies et al, 2015). This parameter was used to automatically classify the different constituents (as bubbles, diatoms and flocs) of the SPM and permitted to select flocs only: low aspect ratios (AR<0.1) were used as selection criterion to avoid the analysis of planktonic organisms as diatoms; high aspect ratios (AR>0.9) were used as criteria to remove bubbles (see Fig.…”

A Multidisciplinary Approach for Generating Globally Consistent Data on Mesophotic, Deep-Pelagic, and Bathyal Biological Communities

“…In the future, we expect that highvolume species identification and quantification methods, such as automated environmental DNA (eDNA) sampling and metabarcoding techniques (Bucklin et al, 2016), will prove particularly useful, augmented by automated image identification with high-volume video/holographic plankton recorders (Davies et al, 2015).…”

Particle sources and transport in stratified Nordic coastal seas in the Anthropocene

“…Particle size is one of the most defining parameters, but particle size distribution remains largely unmapped in most of the Nordic coastal ocean and lacking in current monitoring efforts. Today off-the-shelf optical sensors for in situ measurements of particle size distribution exist, including sensors using near forward scattering (Agrawal and Pottsmith, 2000), macroscopic imaging (e.g., Picheral et al, 2010), and holographic imaging (Davies et al, 2015). Some of these sensors could easily be included into existing monitoring programs.…”