Proceedings of the ASME 10th Biennial Conference on Engineering Systems Design and Analysis
ESDA 2010
July 12-14, 2010, Istanbul, Turkey

ESDA2010-24900
POOR-CONTRAST PARTICLE IMAGE PROCESSING IN MICRO SCALE MIXING
F. Gökhan Ergin∗
Scientific Instrumentation

Bo Beltoft Watz
Research & Development

Kaspars Erglis
Department of Physics

Dantec Dynamics A/S
Tonsbakken 16-18
Skovlunde, 2740 Denmark
ABSTRACT
Particle image velocimetry (PIV) often employs the crosscorrelation function to identify average particle displacement in
an interrogation window. The quality of correlation peak has astrong dependence on the signal-to-noise ratio (SNR), or
contrast of the particle images. In fact, variable-contrast
particle images are not uncommon in the PIV community:
Strong light sheet intensity variations, wall reflections, multiple
scattering in densely-seeded regions and two-phase flow
applications are likely sources of local contrast variations. In
this paper, we choose an image pair obtained in a micro-scale
mixing experiment with severe local contrast gradients. In
regions where image contrast is sufficiently poor, the noise
peaks cast a shadow on the true correlation peak, producing
erroneous velocity vectors. This work aims to demonstrate that
two image pre-processing techniques – local contrast
normalization and Difference of Gaussian (DoG) filter –improve the correlation results significantly in poor-contrast
regions.
Keywords: Particle image velocimetry, image processing,labyrinthine instability, local contrast normalization, differenceof Gaussian filter.
INTRODUCTION
Particle image velocimetry has matured over a quarter
century and is now an established tool in fluid diagnostics. The
history and general principles of the technique can be found in
two review articles [1, 2]. The development of the technique
has been tremendous due to relentless advances in laser,
detector, and CPU processor technology: In 25 years, thetechnique evolved from the manual construction of a 2component velocity field from a double-exposed particle image∗

Andrejs Cebers
Department of Physics

University of Latvia
8 Zellu Str.
Riga, 1002 Latvia
on a single photographic paper, to digital storage andcomputation of 3-component velocity fields in liter-sizedvolumes in a time resolved fashion. Along the way, several
other sub-techniques and names, such as Digital-PIV, MicroPIV, Nano-PIV, Holo-PIV, Stereo-PIV, Tomo-PIV have been
introduced to describe certain variations of the technique. In
this paper we restrict our discussions to a monoscopic, planar,
two-component velocity field measurement in a micro-scale
mixing experiment.
Current monoscopic PIV standard is the recording of two
individual time-separated particle images on a double-frame
planar photo-detector array (CCD or CMOS) illuminated by adouble-cavity pulsed laser. The images are then divided into
smaller interrogation regions where the cross-correlation map
for the two consecutive recordings is calculated. Assuming thatthe source density, time separation, interrogation window size
and the image density are optimized, the height of thecorrelation peak depends strongly on the signal-to-noise ratioof the particle images. The pixel value distribution of an image
depends on the scattering mode used in the acquisition: Sidescatter reflection (or refraction for transparent seeding) willoften result in an image with bright particles on a dark
background. Alternatively, forward-scatter illumination, often
used in shadow imaging and transmission microscopy
applications will result in images with dark particles (shadows)
on a bright background. In either case, a good contrast between
the particle and the background will produce a better
correlation peak, and provide an accurate displacement
measurement.
Contrast variations are not uncommon among raw PIV
images and might occur due to several reasons. The first reason

Corresponding author; Gokhan.Ergin@dantecdynamics.com

1

Copyright © 2010 by ASME

is the light source: An ideal laser beam is circular, has an axissymmetric Gaussian intensity distribution in the near-, middle-,
and far-field with an M2 value close to unity. However, the real
beam emitted from the laser aperture is often far from ideal in
all aspects. If these deviations are severe, a laser sheet with
undesirable intensity variations is obtained when the beam isstretched using sheet optics. Another reason for the local
contrast variations is the existence of a boundary. Wall
reflections and densely seeded flows can easily produce
saturated pixels in the detector, and in the limit, the sensor
might be irreversibly damaged. Similar examples are theexistence of larger particles and/or bubbles among the smaller
seeding particles. A third reason is the existence of several
liquids with different absorption coefficients, such as in a
mixing experiment. In this work, the authors choose an image
pair obtained in a micro-scale mixing experiment with severe
local contrast gradients (Figure 1).

Figure 1. Particle image of the magnetic mixing
experiment, displaying the labyrinthine instability between
the magnetic fluid (dark) and solvent (bright region).
Mixing enhancement using magnetic forces has been a
popular technique in micro-scales, as the flows tend to remain
laminar in such scales and the mixing of two miscible fluids isdominated by molecular diffusion. In a magnetic microconvection experiment two miscible fluids, one doped withmagnetic particles, are placed side-by-side in a Hele-Shaw cell,
representing a step-like concentration gradient. Under theaction of a homogenous magnetic field normal to theconcentration gradient, the magnetic fluid is forced into thesolvent manifesting a characteristic labyrinthine instability at
the interface. Once formed, this finger-like pattern smears out
in time due to molecular diffusion (Figure 1). This instability isanalogous to the Rayleigh-Taylor instability resulting from thedensity differences under the action of gravitational field.

perturbations and two-dimensional motion of the interface.
Reference 3 also provides the first experimental evidence of“finger-like” and “tongue-like” structures at the interface, withobservations on the characteristic spatial period of the pattern.
Cebers [4] performs a linear stability analysis, introduces amagneto-viscous number, which is the ratio of magnetic toviscous forces, and illustrates that the fingering phenomenon isobserved when the magnetic field strength exceeds a thresholdvalue. Igonin & Cebers [5] provide a comprehensive review on
the subject with an extensive list of references: they generalize
the problem by considering a miscible magnetic fluid pair andperform a linear stability analysis, with analytical results for the
sharp interface and numerical results for the diffuse
concentration distribution. Derec et al. [6] observe “longfingers” under the effect of milder magnetic fields and“splitting fingers” under the effect of stronger magnetic fields
in an experiment validating the observations of Maiorov &Cebers [3]. The parametric study reveals that time; cell
separation distance and magnetic field strength individuallyaffect the characteristic wavelength of the fingers. The
motivation of this work is to provide the first quantitative data,
i.e. the velocity field associated with the labyrinthine
instability.
EXPERIMENTAL SETUP
The experiments are performed using a micro-PIV systemmanufactured by Dantec Dynamics (Figure 2). The system
consists of a HiPerformance inverted fluorescent microscope,
HiSense MkII high-quantum efficiency, double-frame CCD
camera with 12-bit image resolution, MicroStrobe pulsed LED
illumination device; system controller and advanced
synchronization unit with 12.5 ns time resolution. The system
controller is equipped with a Dual Xeon 2.33 GHz Quad core
processor, 4GB RAM running Windows XP operating systemand Dynamic Studio v2.10.86 software platform for dedicatedsynchronization, hardware control, database management,
image processing library and PIV analyses.

The labyrinthine instability has been subject to extensive
theoretical and experimental research. Maiorov & Cebers [3]provide a mathematical formulation assuming evenFigure 2. Dantec Dynamics MicroPIV system.

2

Copyright © 2010 by ASME

The Hele-Shaw cell was prepared using two cover glasses
separated with 127 µm-thick Parafilm. The channels for fluids
and air was cut in Parafilm and cured by heating after
assembly. The pure solvent is prepared by mixing 100 µl of
fluorescent particles (2% solid) in 10 ml of distilled water. The
colloidal solution of magnetic nanoparticles is prepared byadding fluorescent micro-particles in the magnetic fluid. The
water based magnetic fluid was produced from γ-Fe2O3
magnetic particles with a nominal size of 10 nm. This isachieved by precipitating anionic magnetite from aqueous
solution of Fe2+ and Fe3+ chlorides using ammonium hydroxide.
A few drops of magnetic fluid were introduced on the right sideof cell using a micropipette, until the capillary forces filled one
half of the cell with magnetic fluid. Then, few drops of puresolvent were introduced from the other side until the puresolvent came in contact with the magnetic fluid. The
fluorescent seeding particles are carboxilate-modified
microspheres with 1 µm diameter. The particles are Nile-red
fluorescent (between 535-575 nm) and are quite suitable forhigh-resolution MicroPIV applications. The particles are
introduced both in the magnetic fluid and the solvent as lightscatterrers. No attempt was made to differentiate the particles inthe two regions during PIV analysis.

visible as shadows. The image pair is acquired several seconds
after the colloidal solution and solvent are brought in contact.
RESULTS
Without prior image processing, conventional advanced
PIV algorithms fail in the darker regions where the image
contrast is poor. The vector field obtained using an adaptive
correlation algorithm with 2 refinement steps, 2 pass per step,
32-pixel-wide square interrogation windows, and 50% overlap
is shown in Figure 4. No Fourier window, filter, peak validation
or local smoothing is applied in order to have a basic
comparison of vectors in the FOV. It is necessary to perform
image pre-processing to obtain valid vectors in the regions
shaded by the magnetic nanoparticles. Same pre-processing
scheme is applied to both frames of the particle image pair.

Figure 4. Multi-step, multi-pass adaptive correlation
results performed on the raw image pair. Every second
vector is displayed.

Figure 3. The Hele-Shaw cell placed in the center ofthe electromagnet.
The Hele-Shaw cell was placed in the center of an electromagnet, a 38-turn-coil of 0.7-mm-diameter wire (Figure 3). A
current of 2A from Kepco BOP 20-10M power supply
produced a magnetic field strength of 1.8 mT in the central partof the coil. The field was applied before pure solvent mixed
with magnetic fluid.
The second frame of the raw image pair acquired with thissetup is shown in Figure 1. In this acquisition, the field of view(FOV) is 1.6 mm x 1.2 mm using a 10x objective. The trigger
rate is set to 3 Hz; time between pulses is 81,68 ms, first andsecond pulse duration is 450µs, and 550 µs respectively. The
dark region is the colloidal solution of magnetic nanoparticles
and fluorescent particles. The bright region is the pure solvent
containing 1-µm-diameter fluorescent particles, which areContrast normalization [7] (also called pixel value
normalization) is a fairly common image enhancement
technique, simply rescaling all pixel values in an image to
exploit the full range of values supported. For a typical 12-bit
image this could be described as:

pnew = 4095

pold − pmin
pmax − pmin

where pold and pnew describe old and new pixel values, whilepmin and pmax describe the smallest and highest pixel values
present in the original image. After contrast normalization thedarkest and brightest pixel values are guaranteed to be 0 and
4095 respectively. Local contrast normalization is similar; theonly difference is that pmin and pmax are not derived from the fullimage, but from a local neighborhood of pixels in the vicinityof the pixel being scaled. This is accomplished by means of themorphological operations of opening and closing, where theneighborhood is determined by the size and shape of thestructuring element. In this example a round structuring
element was used, which is 15 pixels in diameter. To reduce the3

Copyright © 2010 by ASME

influence of noise a 3x3 median filter was applied prior to themorphological operations. In addition to this, a 9x9 medianfilter is applied repeatedly as an edge-preserving smoothingfilter (Figure 5).

Figure 6. Processed particle image in Figure 1, after
the application of local contrast normalization and DoG
filter.

Figure 5. Processed particle image in Figure 1, after
the application of local contrast normalization.
The processed images still contain some noise after the
local contrast normalization operation. For example, the darkstrips in Figure 5 appear on the regions of steep pixel value
gradients in the raw image in Figure 1, as a result of the fixed,
15-pixel-diameter structuring element. More sophisticatededge-detection algorithms could be used to make an adaptivediameter structure, but this is not in the scope of this work. It isnecessary to apply a high-pass filter to highlight particle
images. The preferred filter used here is a so-called, Differenceof Gaussian (DoG) filter, implemented by convolving theimage with a 7x7 kernel shown in Table 1, and then dividing by
a common divisor; 636.
-1
-6
-15
-20
-15
-6
-1

-6
-20
-26
-24
-26
-20
-6

-15
-26
31
84
31
-26
-15

-20
-24
84
176
84
-24
-20

-15
-26
31
84
31
-26
-15

-6
-20
-26
-24
-26
-20
-6

The vector field obtained from the processed images using
the same adaptive correlation algorithm is shown in Figure 7.
Every second vector is displayed in both directions and, again,
no window, filter and smoothing function is applied. However,a mild local median validation scheme with 63% acceptancefactor is applied in a 3x3 neighborhood to replace the spurious
vectors (1% of total). The corresponding vorticity map is also
displayed with warm colors indicating positive, cold colors
indicating negative and the green color indicating zerovorticity.

-1
-6
-15
-20
-15
-6
-1

Table 1. Coefficients of the 7x7 convolution kernel for
the DoG filter.
The DoG filter is in fact the difference between two
images, each derived by applying different Gaussian filters to
the same original image. The convolution kernel shown in
Table 1 corresponds to a 5x5 Gaussian minus a 7x7 Gaussian.
The processed particle image after the contrast normalization
and DoG filter operations is shown in Figure 6.

Figure 7. Multi-step, multi-pass adaptive correlation
results performed on the processed image pair. Every
second vector is displayed overlaid on the vorticity field.
It is clear from Figure 7 that the proposed image preprocessing scheme has enabled the calculation of valid vectors
in the dark regions of the raw particle image. In order to have avalidity check for the algorithm, the difference of vector maps
with and without image processing can be used. The difference
vector map shows small deviations in the lower left corner of4

Copyright © 2010 by ASME

the FOV and large deviations in the rest (Figure 8), as
expected.
ACKNOWLEDGMENTS
The authors wish to thank O. Petrichenko for ferrofluid
sample, ESF support for PhD studies (K.E.) and project
ESS2009/86 for financial support.

Figure 8. Difference between vector fields obtainedwith and without image processing. Every second vector
is displayed overlaid on the scalar vector length.
CONCLUSIONS AND FUTURE WORK
The potential of image processing functions in obtaining
vector maps from variable-contrast particle images is
illustrated. In this particular work, local contrast normalization(or pixel value normalization) and DoG filter is used to obtainclean particle images for a magneto-hydrodynamic mixing
application. This experiment provides the first quantitative
MicroPIV velocity field measurement for the labyrinthine
instability. Future work includes a parametric investigation ofthis instability with quantitative velocity data and the temporal
evolution of the interface. The techniques applied here are notclaimed to be universal, i.e. they may or may not be applicableto other image pairs obtained in other applications. Moresophisticated algorithms will be developed to have an adaptive
technique that may tackle many different types of contrast
variations. Combined with an advanced edge detection
capability, these algorithms may be applicable to flame front
investigations in combustion diagnostics.

REFERENCES[1] Adrian, R. J., 2005, “Twenty years of particle image
velocimetry”, Experiments in Fluids, Vol. 39, pp. 159-169.
[2] Willert, C., 2009, “Recounting twenty years of digital
PIV, its origins and current trends”, Proceedings of 8th Int.
Symposium on Particle Image Velocimetry – PIV09,
Melbourne, Australia (August 2009)[3] Mairov, M. M. and Tsebers A. O., 1983, “Magnetic
microconvection on the diffusion front of ferroparticles”,
Magnetohydrodynamics, Vol. 19, pp. 376.
[4] Cebers A., 1997, “Stability of diffusion fronts ofmagnetic particles in porous media (Hele-Shaw cell) under theaction of external magnetic field”, Magnetohydrodynamics,
Vol. 33, pp. 48.
[5] Igonin, M. and Cebers A., 2003, “Labyrinthine
instability of miscible magnetic fluids”, Physics of Fluids, Vol.
15, No. 6, pp. 1734-1744.
[6] Derec, C., Boltenhagen, P., Neveu, S., and Bacri J.-C.,
2008, “Magnetic instability between miscible fluids in a HeleShaw cell”, Magnetohydrodynamics, Vol. 44, No. 2, pp. 135142.
[7] Westerweel, J., 1993, “Digital Particle Image
Velocimetry – Theory and Application”, Delft University Press.

5

Copyright © 2010 by ASME