11TH INTERNATIONAL SYMPOSIUM ON PARTICLE IMAGE VELOCIMETRY – PIV15
Santa Barbara, California, September 14-16, 2015

Pixel-accurate dynamic masking and flow measurements around small
breaststroke-swimmers using long-distance MicroPIV
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1

F.G. Ergin , B. B. Watz and N. Wadhwa
122Dantec Dynamics A/S, Tonsbakken 16-18 Skovlunde, DK
Gokhan.Ergin@dantecdynamics.com

Technical University of Denmark, Department of Physics and Center for Ocean Life, Kgs. Lyngby, DK

ABSTRACTMicroorganism locomotion is often the focus of biophysics studies, as these organisms move effectively through water,
within the constraints of low Reynolds number locomotion. Optimal locomotion is required not only to conserve energy,
but also to minimize flow disturbances to avoid detection by predators. In fact, some small organisms switch to a different
swimming mode during growth, as a result of drastic morphological changes in their body. Several MicroPIV / PTV studies
have been performed to quantify the flow fields and disturbances generated by the propulsion of small organisms. Recently
the hydrodynamics of a ~220-µm-long Acartia tonsa nauplius were analyzed in Ref. 1 using time-resolved MicroPIV/PTV,
where a two-step masking technique was applied to remove the organism from the particle images. In the current study, we
use the same particle image ensemble but take a different approach for masking and velocimetry. The new three-stepmasking approach reveals two peaks in the swim velocity, each corresponding to one of the two power strokes. The
organisms swim velocity results indicate that the nauplius is a periodic swimmer and a counter-rotating vortex system isobserved during the power stroke indicating a toroidal vortex in three dimensions.
Keywords: Time-resolved MicroPIV, Long-distance MicroPIV, dynamic masking, bio-microfluidics
INTRODUCTION
Microorganisms are known to adapt different locomotion techniques based on their size and shape [1,2]. In fact, many
microorganisms switch to a different swimming mode during their own lifecycle due to substantial morphological changes[1,3]. The main reason hypothesized for these transitions is the changing Reynolds number. At the intermediate Reynolds
numbers experienced by many marine organisms such as copepods and mollusks, the viscous and the inertial forces havethe same order of magnitude. As the organism grows, the inertial forces become more important compared to the viscous
forces, and a need for a different swimming mode arises. The microorganisms find the most efficient locomotion technique
for their size in order to conserve energy and minimize flow disturbances. There is also a strong motivation for research on
biologically inspired micro-vehicles as there is a growing demand for micro vehicles moving in air and water for different
purposes. Quantifying the hydrodynamics of the locomotion of small organisms can provide the much-needed input todevelop micro scale robotic vehicles. Recently different parameters have been used to quantify the hydrodynamics andenergetics of copepods [1,4,5]. These parameters include spatial and temporal decay of flow disturbance, energy
dissipation, energy budget, and propulsion efficiency. These parameters require the accurate measurement of the flow fieldstructure in a time resolved fashion. Time-resolved MicroPIV has great potential in quantifying flow fields aroundmicroorganisms in the realm of low Reynolds number locomotion.
Several MicroPIV / PTV studies have been performed to quantify the flow fields and disturbances generated by the
propulsion of small organisms [1,6-9]. A few challenges still exist in such measurements: First of all, the object in questionis most likely present in the flow field during the measurements. Since we are only interested in the flow field information,
the successful removal (masking) of the object from the raw particle images is desired, without removing any useful
information from the flow field. Second challenge is about the illumination. Most time-resolved MicroPIV systems use apowerful, pulsed laser as illumination. This type of illumination can easily disable the organism or alter its normal
locomotion behavior. A third challenge is related to repeatability of experiments to obtain reliable results. Performing
measurements around biological objects means that there is very little control on the locomotion direction, and recapturingof certain dataset is extremely difficult, if not impossible. This requires very accurate PIV algorithms to calculate vector
fields from single image pairs. A fourth challenge is related to the timing of the experiment; it is quite difficult to control
when the microorganism will swim through the field of view. Therefore experiments often require special hardware withring image buffers with post trigger functionality. Some of the challenges are addressed and these are explained in thefollowing sections.

Figure 1 Schematic of Acartia tonsa nauplius during a power stroke [1].
EXPERIMENTAL SETUP
The experimental setup is described in Ref. 1 and is summarized here briefly: Copepods Acartia Tonsa were cultured at
18°C and before experiments they were transferred to the test aquarium containing filtered seawater. Only a few individuals
were transferred in order to avoid possible interactions between them. The test aquarium is a glass cuvette (10x10x40mm)
placed on a horizontal stage and kept at room temperature, between 18°C and 20°C. The measurement setup is a longdistance Micro Particle Image Velocimetry (LDµPIV) system in which the light sheet propagation direction and the camera
viewing direction are perpendicular to each other. In biological flows, high-power visible laser illumination is often notpreferred as this can disable the organism, or it can affect the normal locomotion behavior of the organism. For thesereasons, a low-power, continuous-wave infrared laser (Oxford Lasers Ltd, 808nm wavelength) was used for illumination.
Sheet forming optics was assembled to produce a 150µm thick light sheet, defining the measurement depth of theexperiments. TiO2 seeding particles smaller than 2µm were introduced in small quantities until a sufficient seeding density
was achieved. The particle images were recorded on a high-speed CMOS detector (Phantom v210, Vision Research Inc.) ata frame rate of 2000 fps and at a resolution of 1280x800 pixels. The images were acquired with 11.65x magnification
producing a 2.2 mm x 1.4mm field of view (FoV). Single frame image acquisition was performed using Davis software(LaVision GmbH, Göttingen, Germany) with a constant time difference of 500µs between frames. Since the FoV was only
approximately 3mm2, the images were stored in a ring buffer and a manual trigger stopped the acquisition after theorganism had passed through the FoV. Consecutive frames were analyzed for PIV processing - quite typical for timeresolved PIV measurements: 65 frames were analyzed to produce 64 flow field measurements. The first frame of the image
ensemble is shown in Fig. 2, where a 0.2mm tall Acartia Tonsa is visible. From this instant on, Acartia Tonsa propels itself
upwards through filtered seawater by pulling three breaststrokes, covering a distance of approximately 650µm (Averageswim speed approx. 20mm/s). It is observed that Acartia Tonsa moves in an almost-vertical straight line and its angularorientation does not change significantly. Subsequent image analysis is performed using DynamicStudio (Dantec
Dynamics, Skovlunde, Denmark) featuring Matlab link. This includes feature tracking, image masking, velocity fieldcalculation, vector masking and phase-locked averaging.

Figure 2 First frame of the raw particle image ensemble.

DYNAMIC MASKING
Objects and surfaces often appear in PIV images. Unless masked, these contribute to the cross correlation function and as aresult introduce an uncertainty in the PIV calculation of the flow field. It is quite common and straightforward to use static
masks to remove stationary objects, as these can be defined manually. However, it is not as trivial to mask moving objects
or surfaces and literature on dynamic masking is quite limited. In practice, it is often possible to create dynamic masks ofmoving objects by applying a number of image processing functions to the original image ensemble. The idea behind
dynamic masking is to get 0 pixel value on the object that is to be masked and retain the pixel value information
everywhere else. This can be achieved in a two-step procedure. In step one, a new image ensemble is produced by filtering,
thresholding etc. to obtain 0 pixel value on the object and 1 everywhere else. In step two, each time step of the new image
ensemble is multiplied by the corresponding time step of the original ensemble. If the original image background pixel
value is nonzero, this results in a sharp pixel value difference between the mask and the image background. As a remedy,
background subtraction techniques can be used to obtain a 0 pixel value in the mask ensemble’s background. Finally, themask ensemble can be applied to mask either the raw images (image masking) and/or the PIV results (vector masking).
We tried several different dynamic masking strategies in a laboratory-fixed coordinate system, i.e. the real life situation
where the fluid is stationary and the microorganism is in motion. These attempts produced unsuccessful results. Since themicroorganism does not rotate around its axis, we decided to implement a simple tracking method and move to an objectfixed coordinate system, where the microorganism is fixed and the surrounding fluid is in motion. When the object is fixedin the frame conventional static masking techniques can be applied on the images and/or on the calculated vectors. This isachieved in three steps: First, a manual mask is generated using the organism’s image in the first frame (Fig. 2), and thisfeature is tracked throughout the ensemble of 65 images. It was possible to define a constant feature that represents theorganism because some of the seeding particles were stuck on the body and legs (Fig 3).

Figure 3 Close up of the Acartia tonsa showing particles stuck on its body.
The tracked feature was selected as the body of the organism excluding the legs, because the particles stuck on the body
were stationary with respect to the organism and each other. Second, a pixel-accurate tracking algorithm was implemented
as a Matlab script that locates the peak of the cross-correlation function between the defined feature and the entire image(Fig. 4). This essentially searches for the defined feature within the entire image. The algorithm is only pixel accurate
because no subpixel fitting was performed during the computations. Third, when the organism location is established on all
frames, a constant-size region of interest (ROI, 567x384 pix) around it is extracted. The vertical ROI dimension (384pixels) is the maximum value that can be used in all the frames. The limitation is in fact the first and the last frames, where
the distance fore and aft of the organism is defined. The horizontal ROI dimension (567 pixels) is a value that fixes theorganism in the middle of the ROI horizontally and is sufficient to cover until the far field. This three-step procedure fixes
the coordinate system on the organism, and allows the application of a conventional static mask to remove the organism(image masking) and eventually the spurious vectors on the organism (vector masking).
ADAPTIVE PIV PROCESSING
After masking of the organism, the particle displacements were computed using an Adaptive PIV algorithm withrefinement steps, vector validation and deforming windows. The Adaptive PIV is an advanced and iterative crosscorrelation based displacement estimator, where final interrogation windows of 16x16 pixel are used with 50% overlap.
The vectors are considered valid if the peak height ratio is larger than 1.25. In other words, the displacement calculation isconsidered reliable when the highest peak (the assumed signal peak) is larger than 1.25 times the second highest peak(assumed to be noise) in the cross-correlation function. This is certainly not the only vector validation method, but it is oneof the oldest. A threshold value of 1.2 is often used in the literature, so in this respect our threshold value of 1.25 is moreconservative. The subpixel positioning accuracy of the Adaptive PIV algorithm is reported as 0.055 pixels with 95%
confidence [10]. This corresponds to a 94.4 nm displacement in the object space, and the velocity uncertainty is estimatedas 189 µm/s, i.e. 1% compared to the average swim velocity.

Figure 4 Correlation of the organism in the 1st frame.
RESULTSThe organisms swim velocity history can be measured by probing a vector in the far field, upstream of the organism, in abody-fixed reference frame. A time history of this vector shows that the nauplius is quite a periodic swimmer (Fig. 5), andthat three full breaststroke cycles were recorded. One swim cycle period is measured as 10.5 ms (21 frames) and contains
two forward breaststrokes followed by a recovery stroke. A careful investigation of the velocity time history reveals that
there are two peaks during the power stroke, one corresponding to each breaststroke, followed by a full stop and a recess in
the recovery stroke. The two peaks during the power stroke have not been observed previously and it is discovered in thisstudy due to the superior quality of the tracking and dynamic masking process. The maximum swim velocity is recorded as
47.9 mm/s, during the second cycle during the second power stroke. These values are in good agreement with Ref. 1, where
the maximum swim velocity values of 33.7 mm/s and 31.1 mm/s are reported. Additionally, a maximum reverse velocity ismeasured as 24.5 mm/s during the third cycle. In general, the magnitude of the maximum reverse velocity is approximately
half the magnitude of the maximum forward velocity.

Figure 5 Time history of fluid velocity far upstream of the Nauplius in a body-fixed reference frame during 3 breaststrokes.
Finally, a phase-locked velocity averaging is performed over the three cycles and the instantaneous swim velocity value issubtracted to get the flow field details around the organism. Fig. 6 shows the flow field around the organism during thepower stroke (at t=3.5 ms, frame index=8, corresponding to the red dot in Fig. 5) in the vicinity of the maximum swim
speed. The colors indicate the magnitude of the velocity vector. In addition to the wake behind the organism, two vortices
are observed; one on each side of the organism, with opposite rotation direction. The counter-rotating vortex system is anindication of toroidal (ring) vortex system in three-dimensions and is in agreement with the previous findings [1]. The
observed toroidal vortex ring is cleaner in Fig. 6 when compared to Fig 2C and Fig. 2D in Ref 1. This is mainly due to thephase-locked averaging of the flow field. Furthermore, the spatial dimension of the toroidal vortex is similar to the lengthof the organism.

Figure 6 Flow field during power stroke (at the red point in Fig. 2a) Colors indicate velocity magnitude.
CONCLUSIONS & FUTURE WORK
Several important conclusions can be drawn from this experiment: Time-resolved long-distance MicroPIV is a powerful
technique for investigation of flows around microorganisms if a suitable illumination source is used. Low-power infraredlaser illumination proves to be an effective illumination mode, as it does not disable or affect the normal locomotion
behavior of the organism. Furthermore, a new dynamic masking approach is used based on feature tracking. The
importance of image pre-processing and dynamic masking must be stressed once again, as it can produce very different
quality results for the same dataset. Using these, we were able to identify that Acartia Tonsa is a periodic swimmer, anddiscover the double velocity peak during the power stroke for the first time. The existence of the toroidal vortex is also
reconfirmed. The maximum forward swim velocity magnitude was found approximately twice of the reverse swim velocity
magnitude. Finally we can conclude that there is no single dynamic masking recipe that works globally. Dynamic masking
strategies must be custom-made for each image ensemble. Future work includes the investigation of automatic posttriggering options to stop image acquisition in order to avoid long waiting times during the experiment. We also plan to
conduct experiments around microscopic swimmers using a time-resolved Stereoscopic MicroPIV system for 2D3C
measurements.
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