Mark McDowell

Glenn Research Center, Cleveland, Ohio

Elizabeth Gray

Scientific Consulting, Inc., Cleveland, Ohio

Richard B. Rogers

Glenn Research Center, Cleveland, Ohio

An Automatic Phase-Change Detection

Technique for Colloidal Hard

Sphere Suspensions

NASA/TM—2005-213989

October 2005
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Mark McDowell

Glenn Research Center, Cleveland, Ohio

Elizabeth Gray

Scientific Consulting, Inc., Cleveland, Ohio

Richard B. Rogers

Glenn Research Center, Cleveland, Ohio

An Automatic Phase-Change Detection

Technique for Colloidal Hard

Sphere Suspensions

NASA/TM—2005-213989

October 2005

National Aeronautics and

Space Administration

Glenn Research Center
Acknowledgments

The authors thank the group of R. Ottewill, Bristol University, for synthesizing the particles, and W. Russel

and P. Chakin, Princeton University, for providing them to us.

Available from

NASA Center for Aerospace Information

7121 Standard Drive

Hanover, MD 21076

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22100

Available electronically at http://gltrs.grc.nasa.gov

This work was sponsored by the Low Emissions Alternative

Power Project of the Vehicle Systems Program at the

NASA Glenn Research Center.
NASA/TM—2005-213989 1

An Automatic Phase-Change Detection Technique 

for Colloidal Hard Sphere Suspensions 

Mark McDowell 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Elizabeth Gray 

Scientific Consulting, Inc. 

Cleveland, Ohio 44135 

Richard B. Rogers 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Abstract 

Colloidal suspensions of monodisperse spheres are used as physical models of thermodynamic phase 

transitions and as precursors to photonic band gap materials. However, current image analysis techniques 

are not able to distinguish between densely packed phases within conventional microscope images, which 

are mainly characterized by degrees of randomness or order with similar grayscale value properties. 

Current techniques for identifying the phase boundaries involve manually identifying the phase 

transitions, which is very tedious and time consuming. We have developed an intelligent machine vision 

technique that automatically identifies colloidal phase boundaries. The algorithm utilizes intelligent image 

processing techniques that accurately identify and track phase changes vertically or horizontally for a 

sequence of colloidal hard sphere suspension images. This technique is readily adaptable to any imaging 

application where regions of interest are distinguished from the background by differing patterns of 

motion over time. 

Introduction 

Colloidal suspensions of uniformly sized spheres undergo a disorder-order phase transition under 

certain conditions. Such monodisperse suspensions are useful as a physical model of thermodynamic 

phase transitions in conventional engineering materials (Pusey, 1986), (Grier and Murray, 1994), 

(Elliot et al., 1997), (Gasser et al., 2001) and as precursors to photonic band gap materials (Bogomolov 

et al., 1997), (Wijnhoven and Vos, 1998), (Zakhifov et al., 1998), (Subramania et al., 1999). We desire to 

study several aspects of a moving colloidal liquid-colloidal solid interface using conventional optical 

microscopy. This paper describes an approach to automatically track the position and shape of this solidliquid interface. 

Our experiments are the colloidal equivalent of directional solidification techniques in conventional 

materials processing. We use suspensions which closely approximate a hard sphere potential where 

thermodynamic phase behavior is solely a function of volume fraction φ, which is the proportion of total 

volume occupied by particles. Below the freezing volume fraction, φf = 0.494, spheres are disordered and 

free to diffuse throughout the entire sample. Above the melting volume fraction, φm = 0.545, a sample 

becomes crystalline where each particle is “caged” by its neighbors and restricted to movement about a 

lattice point. Between φf and φm is a regime of coexisting colloidal liquid and colloidal crystal 

(Pusey, 1986). In our experiments, we begin with suspensions prepared well below φf and allow either 
NASA/TM—2005-213989 2

slow evaporation or gravitational sedimentation to concentrate the spheres to one end of a cell where 

crystallization occurs. 

When the sphere size falls within a range accessible to optical microscopy, disordered (or “liquid”) 

phases and ordered (or “solid”) phases are clearly visible in the colloidal system. 

The scientific data of interest is extracted from the area of the colloidal sample around the solid/liquid 

interface. Therefore, it is necessary to keep this interface in the microscope’s field of view as it grows. 

Typical interface velocities are on the order of a few microns per hour. Since a single experiment can last 

from days to weeks, it is impractical to require human intervention to ensure that the front remains in the 

microscope’s field of view. Instead, it would be preferable to automatically track the disorder/order phase 

front for a colloidal hard sphere system. We accomplish this by implementing an image processing 

algorithm that is based on the difference between the time-averaged particle behaviors of the two phases. 

This difference in particle behavior leads to differing grayscale levels for the two phases in images 

averaged over appropriate time intervals. We exploit this difference in order to extract the phase 

boundary. 

Most current image processing algorithms track particles by distinguishing them from a background. 

The challenge here is that the spheres are too densely packed for these conventional approaches. The 

volume fractions of the colloidal solid and colloidal liquid at the phase interface are φm and φf, 

respectively. This corresponds to particle separations near the interface that are on the order of 1/10 of a 

particle diameter. In addition, the microscope depth of field is comparable to the particle diameter, so 

particles above and below an ideal focal plane also contribute to most images. This results in images with 

touching or overlapping spheres without even a single pixel to separate them. There is no “background”. 

Typical tracking algorithms based on foreground/background intensity differences will not suffice. The 

phases only differ in the ordering of the particles, which adds another level of complexity to the problem. 

Furthermore, constant illumination by the microscope would cause unwanted thermal gradients. The 

solution must include a means of automatically controlling the microscope to not only move the stage and 

acquire images as the disorder/order phase front grows, but also to control the lamp intensity. 

Some researchers have differentiated colloidal phases by post-processing video data to extract the 

centroid of each particle and then calculating bond orientation order parameters (Grier and Murray, 1994), 

(Gasser et al., 2001). However, this approach is computationally intensive and not feasible for real-time 

control of the microscope. Furthermore, this method is difficult to apply to hard sphere systems because 

centroid extraction typically requires the use of dyed particles and confocal microscopy, and it had been 

found that the dye tends to soften the interparticle potential (Gasser et al., 2001). In contrast, our approach 

can be used with hard sphere particles using conventional microscopy techniques and undyed spheres, 

and the necessary processing steps can be performed quickly enough to be used for stage control 

purposes. 

Materials and Methods 

Before our technique was developed, the optimal way to detect the interface in a colloidal hard sphere 

experiment was to have a person use an image digitizer and trace the contour of the interface manually, 

which is extremely time consuming and is prohibitively expensive due to the large number of images that 

represent a typical colloid experiment. A typical experiment can consist of hundreds to thousands of 

images. Consequently, we have developed, tested and implemented a technique that will accurately detect 

the interface of colloidal hard sphere experiments automatically and continually over long time periods 

without human intervention. What follows is a brief overview of our experimental procedure and an 

explanation of our interface detection technique. 

Experimental Setup and Image Acquisition 

The colloidal particles were 1.1 μm diameter poly (methyl methacrylate) (PMMA) spheres with a 

grafted layer of poly (12-hydroxy stearic acid) (PHSA) chains. These spheres were dispersed in a 
NASA/TM—2005-213989 3

combination of organic solvents, decalin, and tetralin, to closely match the sphere and fluid indices of 

refraction in order to avoid sample turbidity at the high volume fractions of interest. Such suspensions 

have been shown to closely follow the hard sphere equation of state (Phan et al., 1996). Comparison with 

test particles embedded in optical epoxy of known index of refraction suggests that ∆n/n<0.01 for our 

suspensions. Since particle separation increases as the interparticle potential is “softened” from the hard 

sphere potential, experiments with hard spheres represents the most challenging image processing 

scenario for phase interface detection. 

The experimental cell was constructed by placing a measured drop of suspension between a standard 

microscope slide and coverslip and sealing the edges with epoxy. This produced a test section that was 

~25 μm thick. A slow evaporation from one side of the cell resulted in a volume fraction gradient which 

produced a moving solid/liquid interface. This served as a precursor experiment to ongoing work using 

the more controlled conditions of gravitational settling. We used an upright biological microscope with a 

63x/1.3NA objective in the transmitted light differential interference contrast (DIC) mode. We have also 

been successful in applying this technique using phase contrast optics. Though the Rayleigh criterion 

would suggest a resolution limit of about 1/4 μm at this numerical aperture, we find that practical imaging 

with such closely index-matched particles is limited to particle diameters of about 1/2 μm and larger. 

Images were acquired from the center of the cell volume at the solid/liquid interface using a CCD camera 

and recorded on videotape at 30 frames per second. Following the experiment, sequential images were 

acquired from the videotape and stored as individual files for processing. 

Detecting the Colloid Interface 

The colloidal system consists of two phases, a solid or ordered phase and a liquid or disordered phase. 

These two phases are separated by a narrow transitional region predicted by theory to be about 3 to 7 

particle diameters wide for systems with a hard sphere potential (Curtain, 1989; Chowhury, and Ghosh, 

1998). Our technique locates the interface between ordered (“solid”) and disordered (“liquid”) regions in 

a colloidal suspension. The technique uses frame averaging, brightness slicing, filtering, and a particle 

finding algorithm on a series of image files. The orientation of the growing crystal is left to right (figure 1 

vertical interface) where the colloidal liquid occupies the rightmost 1/4 of the image while the colloidal 

solid occupies the leftmost 3/4. Four different crystal grains lie along the solid side of the interface, and 

each grain has a different crystallographic orientation, as deduced by human visual inspection. 

Frame averaging 

The core of our interface detection technique is based on the fundamental difference between the 

behavior of individual spheres in colloidal liquid and the colloidal solid phases. In the liquid phase, the 

spheres are not localized. Rather, there is sufficient room for them to diffuse freely throughout the liquid 

phase. In contrast, spheres in the solid phase are arranged in 3-D crystalline order, and the spacing 

between particles is so small that movement is limited almost exclusively to a small “cage” defined by 

nearest neighbors. This allows us to use frame averaging to separate solid from liquid. When a number of 

frames are averaged, the liquid portion tends to become blurred or grayed out while the solid portion 

maintains or increases its clarity and contrast. 

The time scale of the image sequence for frame averaging is set by the characteristic time scale of the 

particle motion. This can be determined by calculating the average time required for a particle to diffuse 

its radius, α (Poon, 1995). The mean square displacement of a colloidal particle, E2

, is given by the 

Einstein-Smoluchowski equation: 

E = 6Dτ 2 (1) 

where τ is the time duration considered and D is the diffusion coefficient given by the Stokes-Einstein 

relation, 
NASA/TM—2005-213989 4

π η = a

k T D B

6 (2) 

where kB is Boltzmann's constant, T is the absolute temperature and η is the viscosity of the suspending 

liquid. By combining equations (1) and (2) and using the square of the particle radius for E2

, the time to 

diffuse a particle radius, τa, is 

k T

a

B

3 πη τα = (3) 

For our colloidal suspension, τa = 0.3s. We expect that our technique will require time intervals equal 

to or greater than τa. 

Frame averaging was performed by summing the values at a pixel location over a series of frames and 

dividing by the number of frames. Equation (4) gives the output pixel value at row i, column j based on a 

series of n input images. 

n

I

O n

ij

ij

∑

= (4) 

i,j: row and column position of the pixel in a frame (image) 

n: number of frames 

I: input pixel 

O: output pixel 

Figures 1 and 2 give an example of a typical image and the averaged image over a series of frames. 

Note the loss of contrast in the liquid portion of the averaged image. 

Brightness slicing 

The brightness slicing technique is used to create a high-contrast image that specifically highlights a 

particular object of interest and works well in separating objects that have either light or dark values from 

a background with intermediate values. Brightness slicing is performed utilizing user-defined threshold 

values. The technique is performed on a series of frame-averaged images and when applied, the result will 

be a solid region highlighted in white against a black background, thereby making the solid-liquid 

interface location easy to determine. 

More specifically, brightness slicing is a double binary contrast enhancement operation where userdefined pixel intensity values below a lower threshold and above an upper threshold are set to white, 

while values between the two thresholds are set to black. Ideally, the liquid or blurry region is set to black 

while the solid region is set to white. Equation (5) gives the formula for determining the output pixel 

value at row i, column j given the user-defined upper and lower threshold values. 

Oij = 0, α1≤Iij≤α2 (5) 

 = 1, otherwise 

α1: lower threshold value 

α2: upper threshold value 

This operation refines the averaging operation by further differentiating liquid from solid regions with 

the result being a mostly white solid region and a mostly black liquid region. Because the solid region 

contains some of the same intensity values as the liquid regions, some erosion of the particles may occur. 
NASA/TM—2005-213989 5

Figure 3 illustrates a typical colloid image after utilizing our brightness slicing technique. Notice there is 

now a distinctive separation pattern between the solid and liquid regions of the image (McDowell, 2003). 

Clean technique 

This technique cleans the image using a center-deleted spike filter modified to remove noise not 

associated with particles. It is designed to work on images that consist of light particles on a black 

background and is described by (Crouser, Bethea, and Merat, 1997). The clean filter works by summing 

the pixel values surrounding a central pixel of interest. The center pixel is not included in the sum. If the 

sum is less than an adjustable clutter threshold, the area is determined to be background clutter (i.e., not in 

the vicinity of a particle) and the pixel of interest is set to 0. The pixel value is unchanged if the sum is 

greater than or equal to the clutter threshold. Two additional parameters are present, the filter size and the 

significance threshold. The filter size designates the extent of the neighborhood around the current pixel 

that will determine its output value. The significance threshold is a way to disregard low level noise. 

Pixels with a grayscale value less than the significance threshold are not included in the sum. The clean 

filter is an effective method for cleaning up background and spike noise while not attenuating particles. 

Mathematically this is defined as: 

r(i,j) = ΣΣ f(k,l) * g(i+k, j+l) * H(g(i+k, j+l) -λ1) (6) 

where: k,l are elements of {–1,0,1} for a 3 by 3 filter 

h(i,j) = g(i,j) * H(r(i,j) -λ2) (7) 

where 

H(x) = 0 if x<0 (8) 

 = 1 otherwise 

g(x,y) = the grayscale pixel value of the pixel located at (x,y) 

f(k,l) = the filter kernel value at (k,l) (0 at k = 0, l = 0, 1 everywhere else) 

r(i,j) = the output of the convolution step 

h(i,j) = the output of the filtering operation = the new value of the pixel at (i,j) 

λ1 = a significance threshold for each pixel in the summation 

(significance Threshold) 

λ2 = the output threshold for the filter (clutter Threshold) 

Applying the clean technique after the brightness slice operation greatly attenuates the noise in the 

liquid region while leaving the solid region largely unchanged (fig. 4). Note that the processing 

techniques have worked equally well for each of the four crystallographic orientations represented along 

the interface. 

Image dilation technique 

After the brightness slice and clean operations, the solid portion of the image is made up of many 

individual white pixels. The “find particles” technique will only find particles that are 8-connected. An 8-

connected object has a continuous perimeter that can be traced by traveling in 8 directions (up, down, left, 
NASA/TM—2005-213989 6

right, and diagonals) and at least one pixel separating the object from the image edge. It is necessary to 

dilate the white pixels to ensure that they will form a connected mass. For this experiment, the best 

dilation technique was a 7 by 7 low pass filter with zero at the center and larger weights away from the 

center (fig. 6). 

The low pass filter is applied to the cleaned image resulting in dilation of the individual white 

particles to form a solid mass (fig. 7). After the dilation operation, a thin strip along the borders of the 

image is blacked out to ensure the solid region will be separated from the image edge. 

Find particles technique 

After image dilation, a find particles algorithm described by (Bethea, 1996) is applied. This technique 

scans an image and identifies every non-background object. This function searches a grayscale image for 

non-background particles using an eight directional search (N, NE, E, SE, S, SW, W, and NW) and looks 

for the largest non-background particle in the image, which represents the transition mapping of the 

solid/liquid interface. 

Figure 7 shows the result of the find particles algorithm. Notice the algorithm found one very large 

particle representing the entire solid region. The particle perimeter is identified and the particle 

number (1) is drawn at the intensity-weighted center of mass of the particle. 

Interface determination 

The largest particle found is determined to be the solid region. Depending on the orientation of the 

sample, the topmost pixel in each column or the rightmost pixel in each row is taken as the solid/liquid 

interface. The automated interface solution is superimposed on the original unprocessed image with a 

contrast line as shown in figure 8. The data from this line is stored in a database and associated with the 

corresponding image file. 

Results 

In order to verify the accuracy and reliability of the interface detection technique, we conducted three 

separate analyses of a sequence of images taken from a colloid hard sphere experiment. We chose a 

subset of 10 images with a 0.33 second time interval between each image. This time interval was slightly 

greater than τa for our colloidal system. In line with expectation, our technique did not work properly for 

time intervals less than τa. The average velocity of the interface for this experiment was 2.6 microns/hr. 

Therefore, in 0.33 seconds, the net growth of the interface would only be about 1/4 nm, which is 

essentially a stationary interface over the duration of the image sequence. Figure 9 shows an example of 

the first, fifth, and tenth image in the sequence using τa. The sequence of images contains the interface 

region between a colloidal crystal and a colloidal liquid. The colloidal crystal is located on the left portion 

of the images and the colloidal liquid is located on the right portion of the images with a roughly vertical 

interface in between. The crystal/liquid interface is defined by the transition from ordered spheres to 

disordered spheres. 

The analysis was conducted as follows. First, two of the authors made a manual determination of the 

interface location for each image in the series by tracing a line over where they believed the interface was 

located. Second, the images were given to three independent experts who also made manual 

determinations of the interface location for each image in the same manner. Third, the images were 

analyzed by the machine vision technique described earlier in this paper, which generates an automated 

determination of the interface location in each image. Figure 10 shows the first, fifth, and tenth images 

analyzed by the automated interface technique. Figure 11 shows the average of the authors’ interface 

locations compared to the automated interface technique. Figure 12 shows the average of the experts’ 

interface locations compared to the automated interface technique. For comparison purposes, when using 

a human observer’s determination of the interface location, the midpoint of the traced line was taken. 
NASA/TM—2005-213989 7

Table 1 shows the comparative analysis of image stability using the first image in each sequence as a 

baseline. The data shows that the automated interface technique has the smallest position change on 

average by a factor of 2 to 5 compared to the manual results. For each image, the distance from a given 

human observer’s interface to the automated interface was found simply by taking the distance for each 

row of the image and averaging. The comparative analysis between our automated interface technique 

calculations and the authors’ manual calculations is listed in table 2. The comparative analysis between 

our automated interface technique calculations and the independent experts’ manual calculations and the 

data is listed in table 3. 

Discussion 

The main finding of this research is that the automated interface algorithm appears to be more reliable 

at identifying the transition zones in our microscopy images than the human experts. Theoretically, there 

should not be a significant difference in the interface location over the course of the image sequences 

shown in figures 9 through 12 as a result of the slow growth velocity and short time duration chosen, so 

for each method (whether automated or human), the perceived interface location should remain stable. 

The human observers were tested for reliability by finding the distance between the interface location in 

each image as compared to the first image. The same was done for the automated technique. 

Using the authors’ manual data, the average interface pixel difference from the automated interface 

technique was 9.2 pixels. Using the independent experts’ manual data, the average interface pixel 

difference from the interface technique was 10.9 pixels. As can be seen in figures 11 and 12, the largest 

variations occurred for the crystal grain closest to the bottom of the image. However, the largest 

variations between manual results also occurred within this region. This seems to indicate that the 

interpretation of the interface position is more ambiguous for this particular crystal orientation for both 

human observers and our computer algorithm alike. However, the overall agreement between manual and 

automated results is quite good. The sphere diameter in our images was 7.1 pixels, so the average 

variation from author and independent expert results were 1.3 and 1.5 particle diameters, respectively. 

Therefore, the average variation between the automated and manual interfaces was at most only one-half 

the dimension of the theoretical interface width, which is predicted to be approximately 3 to 7 particle 

diameters wide (Curtain, 1989; Chowhury and Ghosh, 1998). 

We found that the interface calculated by our algorithm deviated from the manual results by only 

about one-half the theoretical width of the transition region, and the repeatability of the algorithm results 

was better than that of the manual results. Experiments that would take weeks or even months to analyze 

manually can now be done in a matter of minutes or hours using our automated interface detection 

technique. The interface detection technique described in this paper is directly applicable to any imaging 

application where regions of interest are distinguished from the background by differing patterns of 

motion over time. 

References

Bethea, M.D. 1996. High Precision Algorithms for Stereo Imaging Velocimetry, Ph.D. Dissertation. 

Department of Computer Engineering and Science, Case Western Reserve University. 

Bogomolov, V.N., Gaponenko, S.V., Germanenko, I.N., Kapitonov, A.M., Petrov, E.P., N.V. Gaponenko, 

N.V., Prokofiev, A.V., Ponyavina, A.N., Silvanovich, N.I. and Samoilovich, S.M. 1997. Photonic 

band gap phenomenon and optical properties of artificial opals. Phys. Rev. E 55, p. 7619. 

Choudhury, N., and S.K. Ghosh. “Modified weighted density-functional approach to the crystal-melt 

interface.” Phys. Rev. E 57, no. 2 (1998): 1939–1945. 

Crouser, P.D., Bethea, M.D., and Merat, F.L. 1997. Unattenuated tracer particle extraction through timeaveraged, background image subtraction with outlier rejection. Experiments in Fluids, 22. 

Curtain, W.A. 1989. Density-functional theory of crystal-melt interfaces. Phys. Rev. B 39, 6775. 
NASA/TM—2005-213989 8

Elliot, M.S., Bristol, B.T.F. and Poon, W.C.K. 1997. Direct measurement of stacking disorder in hardsphere colloidal crystals. Physica A, 235, 216. 

Gasser, U., Weeks, E.R., Schofield, A., Pusey, P.N. and Weitz, D.A. 2001. Real-space imaging of 

nucleation and growth in colloidal crystallization. Science 292, 258. 

Grier, D.G. and Murray, C.A. 1994. The microscopic dynamics of freezing in supercooled fluids. J. 

Chem. Phys. 100 [12], 9088. 

McDowell, M. 2003. Stereo Imaging Velocimetry, US Patent no. 6, 603, 535. 

Phan, S.E., Russel, W.B., Cheng, Z., Zhu, J., Chaikin, P.M., Dunsmuir, J.H. and Ottewill, R.H. 1996. 

Phase transition, equation of state, and limiting shear viscosities of hard sphere dispersions. Phys. 

Rev. E 54 [6], 6633. 

Poon, W.C.K. and Pusey, P.N. “Phase transition of spherical colloids. 1995. Observation, Prediction and 

Simulation of Phase Transitions in Complex Fluids, ed. M. Baus et al., 3–51. Kluwer Academic 

Publishers, Netherlands. 

Pusey, P.N. and van Megen, W. 1986. Phase behavior of concentrated suspensions of nearly hard 

colloidal spheres. Nature 320, 340–342. 

Subramania, G., Constant, K., Biswas, R., Sigalas, M.M. and Ho K.M. 1999. Optical photonic crystals 

fabricated from colloidal systems. App. Phys. Lett. 74, p. 3933. 

Wijnhoven, J.E.G.J and Vos, W.L. 1998. Preparation of Photonic Crystals Made of Air Spheres in Titania 

Science, 281, p. 802. 

Zakhidov, A.A., Baughman, R.H., Iqbal, Z., Cui, C.X., Khayrullin, I., Dantas, S.O., Marti, J. and 

Ralchenko, V.G. 1998. Carbon structures with three-dimensional periodicity at optical wavelengths. 

Science, 282, p. 897. 

TABLE 1.—COMPARATIVE ANALYSIS OF INTERFACE STABILITY USING IMAGE 1 AS A BASELINE 

(IN PIXELS) 

 D1–D2 D1–D3 D1–D4 D1–D5 D1–D6 D1–D7 D1–D8 D1–D9 D1–D10 AVG. 

Auto 

Interface 

Technique 0.93 1.19 1.4 1.36 1.7 2.19 2.52 3.19 3.64 2.01 

 

Author 1 7.45 5.07 10.61 4.71 4.56 6.47 4.84 3.95 6.81 6.05 

Author 2 5.92 3.03 4.93 4.35 3.07 3.57 3.83 3.1 5.9 4.19 

 Author Avg. 5.12 

 

Expert 1 8.86 8.82 9.48 13.81 9.48 12.72 8.46 6.92 20.82 11.04 

Expert 2 7.87 12.27 7.56 9.38 8.73 9.41 6.89 8.26 8.51 8.76 

Expert 3 3.74 8.48 4.81 7.89 6.65 7.3 4.29 7.14 5.61 6.21 

 Expert Avg. 8.67 
NASA/TM—2005-213989 9

TABLE 2.—AUTHORS’ CALCULATED DISTANCE FROM THE 

AUTOMATED TECHNIQUE INTERFACE LOCATION 

Interface no. Author 1 

(in pixels) 

Author 2 

(in pixels) 

Averaged 

(in pixels) 

1 6.4 12.3 9.35 

2 7.2 12.3 9.75 

3 7.1 10.0 8.55 

4 7.4 12.7 10.05 

5 6.7 11.2 8.95 

6 7.0 11.6 9.3 

7 6.1 9.4 7.75 

8 7.7 9.7 8.7 

9 7.4 12.1 9.75 

10 7.1 11.6 9.35 

Average 7.01 11.29 9.15 

TABLE 3.—EXPERTS’ CALCULATED DISTANCE FROM THE 

AUTOMATED TECHNIQUE INTERFACE LOCATION 

Interface no. Expert 1 

(in pixels) 

Expert 2 

(in pixels) 

Expert 3 

(in pixels) 

Averaged 

(in pixels) 

1 13.5 13.6 11.30 12.80 

2 15.1 10.8 11.70 12.53 

3 17.6 10 8.60 12.07 

4 11.3 13 10.00 11.43 

5 8.3 10.4 6.90 8.53 

6 10.7 10.8 10.00 10.50 

7 5.7 8.2 7.20 7.03 

8 12.5 11.7 11.00 11.73 

9 13.8 12.3 6.50 10.87 

10 13.8 10.6 11.20 11.87 

Average 12.23 11.14 9.44 10.94 
NASA/TM—2005-213989 10
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NASA/TM—2005-213989 12
NASA/TM—2005-213989 13
NASA/TM—2005-213989 14
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20

An Automatic Phase-Change Detection Technique for Colloidal Hard

Sphere Suspensions

Mark McDowell, Elizabeth Gray, and Richard B. Rogers

Image analysis; Image techniques; Pattern recognition; Microscopy; Transition mapping;

Automated interface detection; Machine vision

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Mark McDowell and Richard B. Rogers, NASA Glenn Research Center; and Elizabeth Gray, Scientific Consulting,

Inc., 17418 Puritas Avenue, Cleveland, Ohio 44135. Responsible person, Mark McDowell, organization code RUB,

216–433–8161.

Colloidal suspensions of monodisperse spheres are used as physical models of thermodynamic phase transitions and as

precursors to photonic band gap materials. However, current image analysis techniques are not able to distinguish

between densely packed phases within conventional microscope images, which are mainly characterized by degrees of

randomness or order with similar grayscale value properties. Current techniques for identifying the phase boundaries

involve manually identifying the phase transitions, which is very tedious and time consuming. We have developed an

intelligent machine vision technique that automatically identifies colloidal phase boundaries. The algorithm utilizes

intelligent image processing techniques that accurately identify and track phase changes vertically or horizontally for

a sequence of colloidal hard sphere suspension images. This technique is readily adaptable to any imaging application

where regions of interest are distinguished from the background by differing patterns of motion over time.