Human Reproduction vol.14 no.4 pp.997–1004, 1999
Effect of image sampling frequency on established and
smoothing-independent kinematic values of capacitating
human spermatozoa
Sharon T.Mortimer1 and M.Anne Swan
Dept of Anatomy & Histology and Institute for Biomedical
Research, University of Sydney, Sydney NSW 2006 Australia
1To
whom correspondence should be addressed at: Dept of Animal
Science, University of Sydney, Sydney NSW 2006, Australia
It is known that image sampling frequency affects sperm
kinematic values, although no study has considered the
relative effect upon hyperactivated and non-hyperactivated
spermatozoa. We determined the relative effect of image
sampling frequency on the classification of capacitating
human spermatozoa, using the established kinematic
measures as well as a series of new, smoothing-independent
kinematic measures. Spermatozoa were prepared by direct
swim-up from semen and sperm movement was recorded
using a video system which gave 201 images/s on freezeframe playback. Trajectories were reconstructed manually
and kinematics were determined using the (x, y) co-ordinates of each track point. Lower image sampling frequencies
were approximated by considering every second, third,
fourth, sixth and eighth track point for each trajectory. Of
the 22 kinematic values investigated, only three were
not significantly affected by sampling frequency in both
hyperactivated and non-hyperactivated spermatozoa. Also,
significant differences were observed between hyperactivated and non-hyperactivated tracks at all image sampling
frequencies studied for all but four kinematic measures.
Key words: human/hyperactivation/motility/spermatozoa
Introduction
Human sperm movement is analysed routinely as part of sperm
function tests in the work-up of an infertile couple, and the
results may have an influence upon the couple’s choice of
treatment. One important aspect of sperm movement which
may predict failure of spermatozoa to penetrate the zona
pellucida is hyperactivation. While many definitions for human
sperm hyperactivation have been published, several of the
kinematic values used in these definitions are known to be
significantly influenced by the image sampling frequency.
Also, curvilinear velocity (VCL; Table I) is a track-averaged
value, meaning that if the sperm movement pattern switched
between hyperactivated and non-hyperactivated during the
analysis window, the VCL value may fall below the threshold
level required for a trajectory to be defined as hyperactivated
(Mortimer and Swan, 1995b).
Recently, a series of ‘new’ kinematic measures of capacitating human sperm movement has been developed (Mortimer
© European Society of Human Reproduction and Embryology
and Swan, 1999; Table II). These new values were derived
independently of the average path, and so were smoothingindependent. This is important, as one of the major differences
between computer-aided sperm analysis (CASA) instruments
is the manner in which the average path is smoothed, and
hence the way in which the smoothing-dependent kinematic
values (such as average path velocity (VAP), straightness
(STR), wobble (WOB), amplitude of lateral head displacement
(ALH) and beat/cross frequency (BCF); Table I) are derived.
It has been postulated that the use of smoothing-independent
kinematic measures will reduce the confusion which currently
exists as to the identification of ‘hyperactivated’ versus ‘nonhyperactivated’ spermatozoa by removing a source of difference between CASA instruments (ESHRE Andrology Special
Interest Group, 1998). Another point of difference between
CASA instruments, and between countries, is the image
sampling frequency used for trajectory reconstruction, which
may vary between 25–60 images/s. The determination of which
kinematic values remained relatively robust over these image
sampling frequencies would allow further selection of the
values which have the highest potential usefulness in the next
generation of CASA instruments.
The influence of the image sampling frequency upon
kinematic values has been demonstrated previously for human
spermatozoa using a first-principles approach (Mortimer et al.,
1988). However, that study did not include a comparison of
the relative effects of sampling frequency upon the kinematic
values of hyperactivated and non-hyperactivated trajectories.
Zhu et al. (1994) showed by comparison of two CASA
instruments that the image sampling frequency (25 versus
30 Hz) influenced the proportion of tracks identified as
hyperactivated. The present study was designed to determine
the effect of image sampling frequency on a range of both
established and new kinematic values for hyperactivated and
non-hyperactivated human spermatozoa. It was important to
establish the effect of frame rate on all of the kinematic
values to determine whether common threshold values for the
definition of a trajectory as hyperactivated could be applied
across a range of sampling frequencies. The aim of this study
was to compare the relative influence of image sampling
frequency upon established and ‘new’ kinematic values, using
both hyperactivated and non-hyperactivated trajectories.
Materials and methods
The tracks used in this study were 1 s trajectories of the same
spermatozoa used in a previous study (Mortimer et al., 1997). Briefly,
a capacitating sperm population was prepared by swim-up from
semen into HTF medium supplemented with 30 mg/ml HSA. Sperm
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S.T.Mortimer and M.A.Swan
Table I. Definitions and abbreviations for ‘established’ kinematic measures
Parameter
Abbreviation
Unit
Description
Curvilinear velocity
Straight-line velocity
Average path velocity
Linearity
Straightness
Wobble
Mean amplitude of lateral head displacement
Maximum amplitude of lateral head displacement
Beat/cross frequency
Dancemean
Mean angle of displacement
Fractal dimension
VCL
VSL
VAP
LIN
STR
WOB
ALHmean
ALHmax
BCF
DNCmean
MAD
D
µm/s
µm/s
µm/s
%
%
%
µm
µm
Hz
µm
°
–
Total trajectory of sperm head/time
Distance between first and last track points/time
Length of derived ‘average’ path/time
Ratio (VSL/VCL) 3 100
Ratio (VSL/VAP) 3 100
Ratio (VAP/VCL) 3 100
Mean width of head movement envelope
Maximum width of head movement envelope
Number of times curvilinear path crosses average path/time
(VCL/VSL)3ALHmean
Mean of angles between vectors along the trajectory
Quantifies the complexity of a curve
Table II. Definitions and abbreviations for ‘new’ kinematic measures
Parameter
Abbreviation
Unit
Description
Maximum instantaneous velocity
Average VINmax
Mean instantaneous velocity
Velocity angle measure
Mean three-point area
Maximum three-point area
Mean of three-point area maxima
VINmax
AVmax
VINmean
VAM
TPAmean
TPAmax
TPAmxmn
µm/s
µm/s
µm/s
rad.µms–1
µm2
µm2
µm2
Highest velocity between consecutive points
Mean of trajectory’s three highest VIN values
Average of local maxima of VIN
Product of VIN and the preceding angle change
Mean area bounded by 3 consecutive track points
Maximum TPA for a trajectory
Mean of the three highest TPA values
movement in 50 µm-deep MicroCell-HAC chambers (Conception
Technologies, La Jolla, CA, USA) was videotaped using a NAC
HSV-200 camera and video recorder system, attached to a Reichert
Univar microscope (Mortimer et al., 1988). A 320 positive-low
phase contrast objective was used, with a 32.5 camera ocular and a
31.5 intermediate. A time/date generator was wired in series, and
embedded a 0.001 s time code onto the videotape during recording.
No motility stimulants were used in this study.
The videotape was replayed in a Panasonic NV-F66A VCR on a
Panasonic TC68A61 TV monitor, giving a final magnification of
31900. The trajectories were plotted onto sheets of overhead projector
film attached to the monitor’s screen. Following normal practice,
only trajectories which were in the central portion of the monitor
screen were reconstructed to minimize track distortion caused by
screen curvature. The (x, y) co-ordinates were determined by placing
the sheets over mm graph paper and noting the co-ordinates to within
0.5 mm. Some of the non-hyperactivated tracks had points too close
together to be able to differentiate them to ù0.5 mm in each direction,
so these were reconstructed at 100 images/s by placing a second
overhead projector film sheet over the first and plotting every other
point onto the top sheet, and at 66.7 images/s by plotting every third
point onto another overhead projector film sheet. The same starting
point was used each time.
To obtain the lower image sampling frequencies, the (x, y) coordinates entered in the spreadsheets were ‘culled’ (Mortimer et al.,
1988). For the hyperactivated trajectories, every second point of the
200 Hz trajectory was taken for 100 Hz, every third point for 66.7 Hz,
every fourth point for 50 Hz, every sixth point for 33.3 Hz and every
eighth point for 25 Hz. A similar procedure was used for the nonhyperactivated trajectories, but the 100 Hz trajectory was used for
the 50 and 25 Hz tracks, with every second and fourth point
considered, while the 33.3 Hz tracks were derived by considering
every second point of the 66.7 Hz trajectories.
There were 23 hyperactivated and 24 non-hyperactivated tracks
studied. All of the kinematic values were calculated for each trajectory
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using the Cartesian methods described previously (Mortimer and
Swan, 1995a). The average path was estimated by 5-point smoothing
for the 25 and 33.3 Hz tracks, 7-point smoothing for the 50 and
66.7 Hz tracks and 11-point smoothing for the 100 Hz tracks. The
number of points used for track smoothing was increased with image
sampling frequency to reduce the influence of individual track points
upon the calculated average path. As the image sampling frequency
increases, the number of track points increases, so if a low number
of points are used for smoothing, the average path will be pulled
towards the track peaks.
The established kinematic values: VCL, VSL, VAP, LIN, STR,
WOB, ALHmean, ALHmax and BCF (Table I) were calculated for
each trajectory, as well as mean angular displacement (MAD; Boyers
et al., 1989); Dancemean (DNCmean; Robertson et al., 1988); fractal
dimension (D; Mortimer et al., 1996); and a series of new kinematic
values (VINmax, VINmean, AVmax, VAM, TPAmax, TPAmean,
TPAmxmn, TPAmax(f), TPAmean(f) and TPAmxmn(f); Table II,
Mortimer and Swan, 1999).
Statistical analysis
Receiver–operator characteristic (ROC) curve analyses were performed on the data for each image sampling frequency to determine
the threshold levels for hyperactivation (Schoonjans et al., 1995).
The effect of image sampling frequency on each kinematic measure
was determined by rank correlation analysis of both the hyperactivated
and non-hyperactivated values. Unpaired Wilcoxon analyses were
used to compare the kinematic values for hyperactivated and nonhyperactivated tracks at each image sampling frequency. All statistical
analyses were performed using MedCalc for Windows (MedCalc
Software, Mariakerke, Belgium).
Results
The changes observed in the trajectories of hyperactivated and
non-hyperactivated spermatozoa are illustrated in Figure 1.
Influence of image sampling on frequency on kinematics
Figure 1. Centroid trajectories of two spermatozoa reconstructed at different image sampling frequencies.
The classification of the trajectories was confirmed by applying
the 60 Hz hyperactivation thresholds for manually-reconstructed tracks (i.e. VCL .180 µm/s and LIN ø45% and
WOB ,50% and ALHmean .6.0 µm or ALHmax .10.0
µm) to the 66.7 Hz trajectories (Mortimer and Swan, 1995a),
and by observation of flagellar beat patterns.
The effect of the image sampling frequency on the kinematic
values was determined by rank correlations, as not all of the
values were normally distributed (Table III). The values which
were not significantly affected by frame rate for both the
hyperactivated and non-hyperactivated tracks were TPAmax(f),
TPAmxmn(f) and VSL. This was expected since the TPA(f)
values were corrected for the frame rate used, and the VSL is
only dependent upon the distance between the first and last
track points and the same starting point was always used. The
kinematic values which were frame rate-insensitive for only
the non-hyperactivated tracks were ALHmean, MADdeg and
VAP, and those which were insensitive for only the hyperactivated tracks were fractal dimension and DNCmean, although
P 5 0.050 for DNCmean so this result was equivocal. While
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S.T.Mortimer and M.A.Swan
Table III. Rank correlation analysis of the effect of image sampling
frequency on kinematic values for hyperactivated and non-hyperactivated
tracks (see Table I for definitions)
Kinematic value
TPAmax
TPAmax(f)
TPAmean
TPAmean(f)
TPAmxmn
TPAmxmn(f)
ALHmax
ALHmean
AVmax
BCF
DNCmean
Fractal
LIN
MAD
STR
VAM
VAP
VCL
VINmax
VINmean
VSL
WOB
Non-hyperactivated
Hyperactivated
r
P
r
P
–0.801
–0.033
–0.859
–0.397
–0.810
–0.036
0.209
0.093
0.836
0.968
0.468
0.594
–0.736
0.020
0.466
0.744
0.104
0.765
0.819
0.786
0.030
–0.753
, 0.001
0.723
, 0.001
, 0.001
, 0.001
0.692
0.022
0.309
, 0.001
, 0.001
, 0.001
, 0.001
, 0.001
0.828
, 0.001
, 0.001
0.258
, 0.001
, 0.001
, 0.001
0.745
, 0.001
–0.805
–0.166
–0.873
–0.448
–0.819
–0.150
–0.291
–0.579
0.828
0.956
0.184
0.040
–0.426
–0.713
0.315
0.927
0.421
0.783
0.811
0.768
0.012
–0.418
, 0.001
0.080
, 0.001
, 0.001
, 0.001
0.108
0.002
, 0.001
, 0.001
, 0.001
0.050
, 0.001
, 0.001
, 0.001
, 0.001
, 0.001
, 0.001
, 0.001
, 0.001
, 0.001
0.898
, 0.001
r 5 Spearman’s coefficient of rank correlation.
VCL was significantly influenced by the image sampling
frequency, the hyperactivated tracks had significantly higher
VCL values than the non-hyperactivated tracks at each frequency analysed (all Z . 5.8, P , 0.0001; Figure 2). The
most commonly-used image sampling frequencies for CASA
are within the range 25–60 Hz, so the VCL values for all the
25–66.7 Hz trajectory reconstructions were analysed by ROC
curve analysis. A common threshold value for hyperactivation
of .142.9 µm/s was derived, with a sensitivity of 94% and a
specificity of 97% (Table IV).
There was no significant difference by unpaired Wilcoxon
analysis between the VSL values for hyperactivated and nonhyperactivated tracks for all the image sampling frequencies
except 67 Hz (Z 5 –2.03, P , 0.01) (Figure 2). Accordingly,
no significant threshold level for hyperactivation could be
identified by ROC curve analysis for the range of commonlyused image sampling frequencies (Table IV).
There was a marked increase in VAP of hyperactivated
tracks between 50 and 66.7 Hz, presumably due to differences
in the magnitude of the fixed-point running average used for
its calculation. The non-hyperactivated tracks were unaffected
by image sampling frequency (Table III and Figure 2). Hyperactivated tracks had significantly higher VAP than non-hyperactivated tracks at 66.7 and 100 Hz only (both Z . 3.5, P ,
0.0001). A significant threshold value for hyperactivation
independent of image sampling frequency could not be established by ROC curve analysis (Table IV).
Corresponding with the increase in VAP at 66.7 Hz, the
ALHmax and ALHmean of the hyperactivated tracks dropped
between 50 and 66.7 Hz (Figure 2). This effect would be
expected if the average path was being pulled towards the
track peaks thereby decreasing the riser height, leading to a
1000
lower ALH value. Both the ALHmax and ALHmean values
were significantly higher for the hyperactivated tracks at each
image sampling frequency (all Z . 5.86, P , 0.0001)
and significant hyperactivation threshold values could be
established across the commonly-used image sampling frequencies for each kinematic measure (ALHmax . 8.6 µm and
ALHmean . 5.5 µm, both 100% sensitivity and specificity;
Table IV).
The velocity ratios LIN and WOB declined significantly with
increasing image sampling frequency, while STR increased
significantly (Figure 2 and Table III). All three of the ratio
values were significantly lower for hyperactivated tracks than
for non-hyperactivated tracks at each image sampling frequency
studied (all Z , –5.60, P , 0.0001).
BCF was highly frame rate-dependent, due to it being
derived using the average and curvilinear paths, and also to it
being a frequency measurement (Table III). While the BCF of
the hyperactivated tracks was significantly higher than that of
the non-hyperactivated tracks at 25 and 33.3 Hz (both Z .
3.30, P , 0.0001), it was significantly lower at both 66.7 Hz
(Z 5 –3.11, P , 0.05) and 100 Hz (Z 5 –3.68, P , 0.0001;
Figure 2). There was no significant difference observed between
the BCF values of the hyperactivated and non-hyperactivated
tracks at 50 Hz. Consequently, no common hyperactivation
threshold value could be determined by ROC curve analysis
(Table IV).
DNCmean increased significantly with image sampling frequency for the non-hyperactivated tracks (P , 0.0001 by rank
correlation analysis) but not for the hyperactivated tracks (P 5
0.050 by rank correlation analysis; Table III, Figure 2). The
DNCmean of hyperactivated tracks was significantly higher at
each image sampling frequency studied (Z 5 5.87, P , 0.0001
for all).
MAD was also frame rate-dependent, with the hyperactivated
and non-hyperactivated values converging with increasing
image sampling frequency (Figure 2 and Table III). The
hyperactivated tracks had significantly higher MADdeg values
than the non-hyperactivated tracks at image sampling frequencies of 25 to 50 Hz (all Z . 4.45, P , 0.0001), with no
significant difference between the 66.7 and 100 Hz values.
The fractal dimension values for the hyperactivated
trajectories were not significantly affected by the image
sampling frequency, but increased with increasing image
sampling frequency for the non-hyperactivated tracks (Table
III and Figure 2). The fractal dimension of the hyperactivated
tracks was significantly higher than for the non-hyperactivated
tracks at each frame rate studied (all Z . 5.86, P , 0.0001),
and a threshold value of fractal dimension .1.22 across the
commonly-used frequencies was established by ROC curve
analysis (Table IV).
VINmax, AVmax and VINmean increased significantly with
increasing frame rate (Figure 3 and Table III), and all were
significantly higher for the hyperactivated tracks at all image
sampling frequencies (all Z . 5.70, P , 0.0001). VAM
increased significantly with image sampling frequency (Figure
3 and Table III). The VAM of the hyperactivated tracks was
significantly greater than that of the non-hyperactivated tracks
at each image sampling frequency studied, although the differ-
Influence of image sampling on frequency on kinematics
Figure 2. Effect of frame rate on kinematics. Values shown are mean 6 SD. The circles joined by solid lines are hyperactivated tracks, the
squares joined by broken lines are the non-hyperactivated tracks.
ence was not as marked at 25 Hz (Z 5 3.79, P , 0.05) as it
was at the other frame rates (all Z . 5.74, P , 0.0001).
The TPA values were highly frame rate-dependent, decreasing significantly with increasing image sampling frequency
(Figure 3 and Table III), due to a relative decrease in the area
bounded by the three consecutive track points (Figure 1). The
TPAmax(f) and TPAmxmn(f) values were not significantly
influenced by the image sampling frequency (Figure 3 and
Table III). All of the TPA values were significantly higher for
the hyperactivated tracks than for the non-hyperactivated tracks
at each frame rate studied (all Z . 5.74, P , 0.0001).
All of the kinematic values for each track obtained at each
image sampling frequency (except 100 Hz) were included in
ROC curve analyses to determine whether a threshold value
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S.T.Mortimer and M.A.Swan
Table IV. Threshold values for hyperactivation determined by receiver–
operator curve (ROC) curve analysis, including all tracks reconstructed at
25–66.7 Hz
Kinematic value
Threshold
Percentage
under ROC
curve
Sensitivity
Specificity
TPAmax (µm2)
TPAmax(f) (µm2)
TPAmean (µm2)
TPAmean(f) (µm2)
TPAmxmn (µm2)
TPAmxmn(f) (µm2)
ALHmax (µm)
ALHmean (µm)
AVmax (µm/s)
BCF (Hz)
DNCmean (µm)
Fractal
LIN (%)
MAD (°)
STR (%)
VAM (rad/µms)
VAP (µm/s)
VCL (µm/s)
VINmax (µm/s)
VINmean (µm/s)
VSL (µm/s)
WOB (%)
.8.10
.452.90
.3.54
.160.17
.7.69
.438.56
.8.6
.5.5
.234.9
.8.6
.10.78
.1.22
ø47
.76.8
ø89
.217.4
.78.7
.142.9
.232.3
.183.0
ø50.9
ø52
96.6
99.6
93.5
99.1
96.7
99.7
100
100
97.9
51.4
100
99.6
98.6
82.2
97.8
89.1
65.5
98.6
97.7
98.9
66.0
98.0
97.8
98.9
90.2
97.8
93.5
98.9
100
100
92.4
83.7
100
100
94.6
68.5
91.3
85.9
41.3
93.5
98.9
94.6
43.5
94.6
83.3
96.9
83.3
93.8
85.4
97.9
100
100
93.8
35.4
100
93.8
93.8
87.5
99
80.2
87.5
96.9
85.4
95.8
89.6
92.7
which would be consistent across all of the commonlyused image sampling frequencies could be obtained for each
kinematic measure. The only kinematic values for which a
consistent hyperactivation threshold could be determined with
100% sensitivity and specificity, irrespective of frame rate
(from 25 to 66.7 Hz) were ALHmax, ALHmean and DNCmean
(Table IV). All of the other kinematic measures had threshold
values with .90% sensitivity and specificity for the range of
image sampling frequencies, except for VAM (89.1 and 85.9%),
MADdeg (82.2 and 68.5%), VSL (66.0 and 43.5%), VAP (65.5
and 41.3%) and BCF (51.4 and 83.7%). Even though the
results from a range of image sampling frequencies were
included, it was interesting to note that the threshold values
of the established kinematic measures determined by ROC
curve analysis were similar to those obtained previously for
the determination of hyperactivated trajectories at 60 Hz
(Mortimer and Swan, 1995a).
Discussion
While the effect of image sampling frequency upon the
perceived kinematic values of a trajectory has already been
reported (Mortimer et al., 1988), no specific attempt was made
in that study to determine the relative effects of image sampling
frequency upon hyperactivated and non-hyperactivated tracks.
Also, the influence of image sampling frequency upon the
‘new’ kinematic values had to be established, to indicate
whether any of these variables were sufficiently robust to
be applied independently of the image sampling frequency
(Mortimer and Swan, 1999). This is an important consideration
in the development of any new kinematic values to be measured
by CASA, since ideally they should be able to be calculated
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in the same way irrespective of the CASA instrument or video
frame rate.
The method used in this study differed from that of Mortimer
et al. (1988) in that all the analyses were performed by
Cartesian methods, using each track’s (x, y) co-ordinates, rather
than by a combination of manual and semi-automated methods.
However, the concept of ‘plucking’ or ‘culling’ track points
to give trajectories equivalent to different image sampling
frequencies had been introduced previously (Mortimer et al.,
1988).
The shape of the trajectories changed with image sampling
frequency, with much more detail observable in the 50–100 Hz
than in the 25 and 33.3 Hz track reconstructions (Figure 1).
This observation provides further justification for the recommendation that trajectory analysis of capacitating human sperm
populations be performed at image sampling frequencies of at
least 50 Hz (ESHRE Andrology Special Interest Group, 1998).
As would be expected, the distance between consecutive track
points was inversely proportional to the image sampling
frequency, with less distance between points with increasing
image sampling frequency.
Image sampling frequency exerted a significant effect on
the values of most kinematic values, and this effect was not
always the same for hyperactivated and non-hyperactivated
tracks (Table II). The only kinematic measures which were
not significantly affected by frame rate or motility pattern
were TPAmax(f), TPAmxmn(f) and VSL. VSL was not affected
by the changing image sampling frequency, as it is only the
distance between the first and last track points, and the same
starting point was used for each image sampling frequency.
The observation of no effect of image sampling frequency is
in contrast to a study comparing VSL measured by different
CASA instruments (Morris et al., 1996). However, in that
study different sampling times, as well as frequencies, were
used and this meant that different track portions were analysed.
Here, the same track portion was re-analysed, so there was no
effect of sampling time. The relative insensitivity of the TPA(f)
values to image sampling frequency demonstrated the success
of multiplication of the three-point area value by the image
sampling frequency to correct for the reduction in distance
between consecutive track points with increasing frame rates,
as had been postulated (Mortimer and Swan, 1999).
For other kinematic measures, i.e. ALHmean, VAP and
MAD, only the non-hyperactivated tracks were not significantly
affected by image sampling frequency (Table III). There was
a marked increase in the VAP of hyperactivated tracks between
50 Hz and 66.7 Hz, presumably because the degree of
smoothing was not sufficient for the 66.7 Hz trajectories, i.e.
they were undersmoothed. Undersmoothing occurs when the
number of points used for the fixed-point running average is
too low, and the average path contains deviations towards the
track apices. This can also result in decreased ALH values, as
the riser distance, the distance between a track point and its
smoothed point on the average path, is reduced. The alternative
possibility was that the 50 Hz path was oversmoothed, with
the smoothed average path being shorter than the ‘true’ average
path. Oversmoothing is the opposite to undersmoothing, with
so many points included in the smoothing algorithm that an
Influence of image sampling on frequency on kinematics
Figure 3. Effect of frame rate on ‘new’ kinematics. Values shown are mean6SD. The circles joined by solid lines are hyperactivated
tracks, the squares joined by broken lines are the non-hyperactivated tracks.
apex’s smoothing will be influenced by the points comprising
an apex on the opposite side of the track. This results in a
generally straight average path, with very minor deviations to
mark the presence of an apex (Davis et al., 1992). Correspondingly, the ALH values from an oversmoothed average path are
higher than the ‘true’ ALH, as there is a greater distance
between a track point and its smoothed point on the average
path.
As predicted by this observation, the ALH values of the
hyperactivated tracks dipped 50–66.7 Hz, indicating a probable
smoothing error (Figure 2). The relative insensitivity of the
non-hyperactivated paths to frame rate indicated that they
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S.T.Mortimer and M.A.Swan
were probably smoothed sufficiently at each image sampling
frequency. The difference between the degree of smoothing
required for hyperactivated and non-hyperactivated tracks was
a further indication of the differences in movement patterns
between hyperactivated and non-hyperactivated spermatozoa.
This observation also illustrated the difficulties encountered
with the use of smoothed values, since even if the correct
degree of smoothing is used for one motility type, it is not
necessarily appropriate for all motility types. In any given
population of capacitating spermatozoa at any given time,
there would be expected to be both hyperactivated and nonhyperactivated spermatozoa, as well as some switching between
motility patterns. If the kinematic values used to classify
spermatozoa were influenced by the average path calculation
then, depending upon the magnitude of the fixed-point running
average used, the same track could be classified differently
depending upon the ALH value obtained.
All of the remaining kinematic values were highly influenced
by the image sampling frequency, regardless of the motility
classification of the trajectory, although many still gave significantly different values for hyperactivated and non-hyperactivated tracks at all image sampling frequencies. The
relationship between hyperactivated and non-hyperactivated
tracks was inconsistent for both MAD and BCF (Figure 2).
The convergence of the MAD values for hyperactivated and
non-hyperactivated trajectories with increasing image sampling
frequency reduced the potential value of this kinematic
measure, as the trend of modern CASA instruments is towards
increasing image sampling frequencies for kinematic analysis.
Also, a theoretical study of MAD has predicted that as image
sampling frequency increases, MAD would decrease, reaching
zero for an image sampling frequency of infinity (Owen and
Katz, 1993). It was presumed that the crossover effect observed
for BCF was probably due to aliasing at the lower image
sampling frequencies. Aliasing occurs when the frequency of
the event being measured exceeds the Nyquist number, i.e.
half the frequency of image sampling frequency (Owen and
Katz, 1993; Davis and Siemers, 1995). The effect of calculation
method and image sampling frequency upon the BCF of a
trajectory will be explored further in another study.
In conclusion, while frame rate affected both the ‘established’ and ‘new’ kinematic measures, discrimination between
hyperactivated and non-hyperactivated trajectories was possible at the image sampling frequencies commonly used by
CASA instruments. Further, independent evaluation of the
applicability of the smoothing-independent kinematic measures
by application of the values in different CASA instruments is
now required.
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Received on April 24, 1998; accepted on February 1, 1999