Journal of Experimental Botany, Vol. 49, No. 327, pp. 1749–1756, October 1998
The use of image analysis and automation for measuring
mitotic index in apical conifer meristems
Lars-Göran Sundblad1,5, Paul Geladi2, Arne Dunberg3 and Björn Sundberg4
1 Forestry Research Institute of Sweden, Box 3, S-918 21 Sävar, Sweden
2 Department Organic Chemistry, Umeå University, S-901 87 Umeå, Sweden
3 OmniVisor, Box 2722, S-762 94 Rimbo, Sweden
4 Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences,
S-901 83 Umeå, Sweden
Received 30 April 1998; Accepted 18 June 1998
Abstract
Introduction
The methodology for determination of mitotic index (MI)
from apical meristems of conifers was improved to
permit the efficient processing of large sample numbers.
Improvements were made at three different stages of
the method. Firstly, hydrolysis, staining, cytoplasmic
bleaching, washing of samples, and temperature
regimes were automated, which reduced the need for
labour and improved the standardization of chemical
treatment. Secondly, the use of vertical and controlled
pressure for squashing improved the quality of the preparations and decreased the fraction of discarded preparations. Thirdly, an interactive image analysis system
for estimation of MI from preparations was constructed.
This system increased the efficiency of analysis of preparations, but did not eliminate subjective manual classification of nuclei into cell cycle stages. The possibility of
using fully automated image analysis for estimation of
MI was investigated using a standard image processing
sequence and by multivariate analysis of image analysis
parameters. For this, principal component analysis (PCA)
was used to detect cell cycle stage related clustering of
nuclei in score plots. PCA was also used to construct a
model based on interphase nuclei that enabled correct
classification of 25 nuclei from five cell cycle stages as
either dividing or non-dividing.
The mitotic index (MI ), i.e. the percentage of cells in a
population undergoing mitosis, has been used to study
cell cycle activity in conifers in relation to several factors,
such as developmental stage ( Fielder and Owens, 1989;
O’Reilly and Owens, 1987), and frost hardiness (Colombo
et al., 1988). Historically, determination of MI in apical
conifer meristems was made from stained, fixed and
longitudinally sliced sections of apices (Owens and
Molder, 1973). Through the use of single cell layer
squashes (Carlsson et al., 1980) and standardized sampling procedures (Grob and Owens, 1994), the time
required for determination of MI has decreased significantly. However, for large sample numbers, the efficiency
in processing and analysis of samples still limits the use
of the technique. In this report, improved methods for
processing, preparation and sample analysis using interactive image analysis are described.
The potential for further methodological improvements
is also demonstrated through multivariate analysis of
image analysis parameters.
Key words: Mitotic index, image analysis, apical meristem, conifer.
Materials and methods
Sample preparation
Buds were sampled from field-grown adult Scots pine trees and
immediately placed in test tubes containing 10% neutral
formalin and stored at 5 °C. Before staining, the bud scales
were gently opened to expose the apical meristem to chemical
treatment. Staining of nuclei, squashing and fixation were
performed essentially as described by Grob and Owens (1994).
However, the complete procedure for staining of nuclei was
5 To whom correspondence should be addressed. Fax: +46 90 150960. E-mail: [email protected]
© Oxford University Press 1998
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Sunblad et al.
done automatically using computer control of temperatures,
stirring and exchange of chemicals. Communication between
the computer and the external equipment was accomplished by
an I/O card and a program written in Turbo Pascal. The
procedure included washing for 24 h in distilled water at 2 °C
with exchange of water every 30 min, hydrolysis in 5 M HCl at
20 °C for 55 min, washing in distilled water at 20 °C, staining
in Schiffs reagent (Fox, 1969) at 20 °C for 2 h, bleaching of
cytoplasm by washing in SO water at 20 °C for 2 h with
2
exchange of SO water every 10 min, washing in distilled water
2
for 10 min at 2 °C and, finally, storage in water for up to 24 h
at 2 °C.
For each staining procedure buds were treated in two
96-well microculture plates which were kept in a water jacked,
stirred vessel (Fig. 1). One bud was kept in each microplate
compartment allowing simultaneous staining of 192 individual
samples. Holes in both the microculture plates and corresponding lids facilitated efficient circulation of chemicals through
microculture plate compartments. Liquids were fed into the
vessel using computer-controlled pumps. Due to the corrosiveness of some of the liquids, pump pressure was mediated to
the liquids by air pressure, thus avoiding direct contact
between pumps and chemicals. Emptying of the vessel was
done by computer control of an electromagnetic valve
controlling a water suction outlet. Different temperatures
during the various steps of the procedure were controlled by
circulating water from a computer-controlled thermostat bath
through the water-jacketed vessel.
After staining, the apical meristem was removed from each
bud and placed in a drop of 45% acetic acid on a microscope
slide. Squashing was made by gently applying a coverslip over
the sample and finally applying pressure to the coverslip, thus
squashing the hydrolysed meristem into a single cell layer
preparation. According to Grob and Owens (1994), pressure
was applied by pressing the rubber tip of a pencil to the cover
slip. This procedure, however, sometimes yielded poor preparations due to smearing if pressure was not applied absolutely
vertically. Furthermore, variations in pressure resulted in
variations in squash characteristics (Carlsson et al., 1980). To
minimize such problems a simple mechanical press was
constructed which applied a vertical pressure to the coverslip.
The pressure was controlled by using an adjustable torque
wrench as the bar handle of the press. The use of a press for
squash preparation gave, according to this study’s experience,
better results than the use of the rubber end of a pencil.
Squashes were fixed by freezing on CO ice, removing the cover
2
slip and extracting water with treatment in 95% and 100%
ethanol for 3 min each. Finally Euparol (Chroma-Gesellshaft,
Köngen/N, 73257 Germany) was applied to the sample before
covering with a cover slip.
Table 1. Image analysis parameters
Original parameters
Definition
Area_Pixels
The summed area of all image points (pixels) that constitute the object.
This also includes the areas of holes within the object.
The summed distances between midpoints of adjacent contour-line pixels.
The longest distance that can be found between the midpoints of two
contour-line pixels.
The length of the object’s projection on a plane perpendicular to Length.
The length of the object’s projection on the X and Y axes of the image
coordinate system.
The shortest and the longest of 32 projections at regular angle intervals of
the object.
The perimeter of an equi-angular polygon with 64 sides, circumscribing
the object.
The area of a circle having Length as its diameter.
The sides of a rectangle having the same area and perimeter as the object.
The mean greyvalue of all pixels included in Area_Pixels.
The mean absorbance value of all pixels included in Area_Pixels,
absorbance being a log transformation of the individual pixel greyvalue.
Perimeter
Length
Width
Feret_X, Feret_Y
MinFeret, MaxFeret
Feretperim
Equarea
Rectangle_A, Rectangle_B
Meangray
Absorbance
Calculated parameters used
in PCA analysis
Definition
Area_Fract
The object’s area as a percentual fraction of the area of the image field.
100.0×(Area_Pixels/fieldarea)
Area_Pixels/Length
Frequently referred to as CIRCULARITY.
(4×p×Area_Pixels)/(Perimeter×Perimeter)
(4×Area_Pixels)/(p×Length×Width)
MinFeret/MaxFeret
Rectangle_B/Rectangle_A
Perimeter excess, i.e. the ratio between perimeter and the perimeter of a
circle having the area Area_Pixels.
Perimeter/Feretperim
Area_Pixels/Equarea
Length/Mean_Width=(Length×Length)/Area_Pixels
Area_Pixels/(Feret_X×Feret_Y )
Area_Pixels/Perimeter
Standard deviation of Meangray.
Standard deviation of Absorbance.
Mean_Width
Form_PE
Form_AR
Form_Feret
Form_Rect
Excess
Roughness
Convex
Elongation
Compactness
Hydr_Radius
Stdev_Gray
Stdev_Abs
Automated mitotic index analysis
Microscopy, video, A/D conversion and image analysis
Microscopy equipment consisted of a transmitted-light microscope (Olympus BH2) with a motorized microscope stage
(Märzhäuser EK32) and motor focus (Märzhäuser MA32). A
monochrome video camera (Cohu 4912) fitted to the microscope
generated images that were digitized by a frame grabber card
(Coreco Oculus-MX 3MB) installed in a host computer
(Compaq 486 Deskpro 33i). For image digitalization, image
processing and automatic object measurements the general
image analysis program VISOR (OmniVisor, Rimbo, Sweden)
was used.
Estimation of MI using interactive image analysis
MI was estimated using MITOS, a specially designed interactive
program based on subroutines from VISOR. In the MITOS
program the following sequence was outlined for estimation
of MI:
(1) Manual approximation of the average nuclear size within
Fig. 1. Set-up for automated staining.
1751
the squash by scanning through the squash and measuring the
area of 10–15 average-sized nuclei.
(2) Interactive specification of squash coordinates.
(3) Subjective manual selection of an image field in the squash
with high quality, i.e. having few overlapping nuclei, few tanninfilled cells etc.
(4) Automatic registration of the image field position from centre
to periphery. Position of image fields was during data analysis
used as a correction factor since mitotic figures were unequally
distributed at different radial positions of the squashes.
(5) Automatic adjustment of video signal gain, digitalization of
the image, contrast normalization, and estimation of the total
number of nuclei within the image field. Calculation of number
of nuclei was achieved by measuring the total area of pixels
having a grey value lower than 128 (of 256) on the normalized
image and dividing this area by the average nuclear area as
defined in (1).
(6) Manual, subjective identification, classification and labelling
of nuclei undergoing mitosis.
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Sunblad et al.
(7) Automatic calculation of MI by combining the automatically
calculated total number of nuclei and the number of manually
identified nuclei undergoing mitosis.
(8) For each squash the sequence from no.3 was repeated, using
new image fields, until estimation of MI was based on at least
200 nuclei per squash.
Methods to investigate the possibility of using image analysis in a
fully automated system for determination of MI
To investigate the potential for using image analysis as the basis
for a fully automated system for the determination of MI, 14
morphometric and densitometric parameters for nuclei in
squash images were recorded by the VISOR program. The
relevance of the recorded parameters for determination of MI
was investigated using two applications of principal component
analysis (PCA) as described below.
Statistical analysis
PCA was first used to obtain an overview of differences and
similarities between different cell cycle stages in terms of image
analysis parameters. Thereafter, PCA was used to construct a
model based only on interphase nuclei, and used to differentiate
between dividing and non-dividing cells. For all multivariate
studies, 14 image analysis parameters were used. To minimize
problems related to variations in illumination of samples under
the microscope or variations in staining intensity, all parameters
except for Area—Fract, were relative parameters, calculated
from original image parameters according to Table 1.
For PCA, the systematic variation in the data
matrix×composed of the variables (i.e. image parameters) and
objects (i.e. nuclei) was described by the model
A
x =x + ∑ t p +e
ik
k
ia ak ik
a=1
or in matrix notation
X=1x∞+TP∞+E
(1)
to unit variance, giving all variables initially the same weight in
the analysis.
Results and discussion
Technical improvements for making microscope
preparations
Compared to the method for determination used by Grob
and Owens (1994), the current method for making microscope preparations has been improved at two stages.
First, treatment of the dissected buds with chemicals has
been automated ( Fig. 1). Secondly, squashing of the
removed apices has been improved by the use of controlled, vertical pressure. Automation resulted in a
decreased need for labour, better control over the staining
procedure and lowered risk for exposure to chemicals,
since automation allowed the staining procedure to take
place under a closed hood. Controlled vertical pressure
reduced the proportion of preparations discarded due to
smearing of squash material by horizontal movement,
but also improved the quality of preparations and resulted
in more standardized squashing. These improvements
might be considered trivial but they were of great practical
importance. For instance, the proportion of preparations
with a quality sufficient for determination of MI was
increased from 60–65% before, to 98% after improvements. Vertical pressure can be achieved either by the use
of simple home-made equipment as reported here, or by
similar commercially available tools.
Interactive determination of MI
(2)
where x denotes an element in the matrix X, i denotes an
ik
index used for objects (nuclei), k denotes an index used for
variables (image parameters), 1 denotes the vector with all
elements being 1, x denotes the kth element of the vector x
k
and is the average for the kth variable, t denotes the principal
ia
component scores (object related) and p principal component
ak
loadings (variable related ) and e is the residual for object i
ik
and variable k. The number of cross terms, A needed to explain
the systematic part in X was determined by cross validation
( Wold, 1978). Furthermore, the parameters P and T were
determined in such a way that the first cross term described the
largest amount of variance in the data, the second cross term
described the second largest amount of the variance in the
data, etc.
Principal component analysis studies the objects and variables
relative to each other in multi-dimensional spaces. Since these
spaces are difficult to see, they have to be projected to planes
that can be plotted. Principal component analysis makes new
axes that allow the construction of these plots in an optimal
way. This is done by calculating new axes that explain maximal
variance in the data. The score plot (Fig. 3) is a projection of
all objects onto a plane spanned by two of these new axes. The
physical dimensions of the scores are undetermined since a
mixture of the dimensions of the original variables is used.
Because of this they are usually called ‘dimensionless’.
Usually, as in the present study, if no prior knowledge of the
relevance of the variables is available, the variables are scaled
Estimation of MI from squash preparations has previously been done by manual counting under a microscope.
Fig. 2. Correlation between MI ((number of cells undergoing
mitosis/total number of cells)×100) estimated by interactive image
analysis and measured by manual counting of nuclei. The figure is
based on 12 image-fields and 472 nuclei.
Automated mitotic index analysis
This is time-consuming and has been a limiting factor for
large-scale use of the technique. With the development of
interactive image analysis the time needed for this process
was reduced by approximately 80%. Estimation of the
total number of nuclei was based on measurement of the
number of pixels in an image field having a grey level
value above a threshold level. This procedure involved
several potential sources of measurement error. Everything in an image darker than the defined threshold
level will contribute to the value for total number of
nuclei and hence tend to decrease the value of MI. In
1753
practice, careful manual selection of suitable areas within
the squash was necessary. In this way areas with high
levels of tissue fragments, elongated nuclei from procambial cells, and nuclei from large tannin filled pith cells were
avoided. The size of nuclei in apical meristems has been
shown to vary over time (Owens and Molder, 1973). This
would affect estimation of MI in the present system if
not taken into consideration, since the estimation of the
total number of nuclei was based on the total stained
area. Therefore, manual estimation of the mean nuclear
area was made for each individual squash by subjectively
Fig. 3. Image processing sequence used for the extraction of the image analysis parameters used in PCA (principal component analysis). (a)
Original image. (b) Figure 3a made binary and inverted. (c) Image in (b) cleaned from objects outside empirically defined acceptance limit values
for size and elongation. (d ) Image in (c) used as a template and applied to image in (a).
1754
Sunblad et al.
selecting 10–15 average sized nuclei for area measurement.
The thus estimated value for mean nuclei area was later
used in the subsequent data analysis as a correction factor
for estimation of number of nuclei. In the present study,
the nucleus area did not show any systematic variation
implying that the above-described procedure might be
excluded. To what extent this might be generally true is
not known. Although variation in staining intensity,
illumination, etc. was low, complete avoidance of this
could not be achieved. This was compensated for by the
use of automatic gain control of the video camera. An
image that was actually dark due to heavy staining or
weak illumination was thus brightened by increased amplification of the video signal. The procedure worked well
in general, but image frames with very few nuclei had to
be avoided since the number of nuclei within such images
tended to be overestimated since large bright areas without nuclei resulted in overcompensation of the videosignal. Squashes normally exhibited an unequal distribution
of mitotic figures, with a higher frequency towards the
periphery. Therefore, the position of each image field
from centre to periphery was registered, and the effect of
position was compensated for in the data analysis.
Having taken the precautions described above, the
correlation between MI measured by interactive image
analysis and manual counting was good (Fig. 2).
Potential for automated classification of nuclei
Although interactive estimation of MI provides efficient
screening of preparations, the technique does not eliminate subjectivity during classification of nuclei. Subjectivity
is a particular problem for pro/interphase and telo/
interphase nuclei classification, since this will influence
the MI estimation. The problem is a consequence of the
continuous transition between different stages of cell
division and this is why classification by definition is
artificial and arbitrary. In consequence, automated classification of nuclei would provide, if not necessarily a more
correct, then at least, a more objective and reproducible
classification.
In Figure 3a–d an image processing sequence is shown,
illustrating a possible basis for a completely automated
system for determination of MI. The original image from
the video camera (Fig. 3a) contained nuclei and other
areas darker than the bright background. The image was
made binary at the grey level in the middle between the
lightest and darkest pixels and thereafter inverted, yielding
an image with two grey levels, a black background and
white objects (Fig. 3b). In this binary image, any group
of connected white image points (pixels) was assumed to
be an object. Area and elongation were recorded from
these objects. Empirically determined acceptance limits
for the two parameters were then used to clean the image
from tissue fragments, contaminating particles, elongated
nuclei from procambial cells and nuclei from large tanninfilled pith cells ( Fig. 3c). Finally, morphometric parameters of the objects remaining in the cleaned binary
image were recorded. The image was also used as a mask
to select areas in the original image for recording densitometric object parameters (Fig. 3d). The procedure
removed not only contaminating cells and particles but
also nuclei clumped together, and large areas with double
cell layers from the image fields. Depending on the quality
of the preparation, a variable number of nuclei were
finally accepted as objects. In all cases large fractions of
nuclei were discarded. This was mainly due to clumping,
i.e. two or more neighbouring nuclei touching or overlapping each other were considered as one large object and
consequently discarded as being too large to fit within
the defined acceptance limits. Degree of clumping was
not related to cell cycle stage. Since squashes normally
consisted of 1000–5000 individual nuclei and only about
350 are required to obtain a good measure of MI (Grob
and Owens, 1994), only about 10% of the individual
nuclei have to be accepted to obtain a stable measure of
MI in an automatic screening system. This concept is
based on the assumption that the risk of being discarded
is equal for nuclei undergoing mitosis and interphase
nuclei. To increase the number of accepted objects, the
method of Bengtsson et al. (1981) for separating neighbouring objects might be included in a fully automated
system. Such procedures should also decrease the risk of
two touching nuclei that have a combined area just below
the upper accept limit for size being erroneously accepted
as a single object.
From the nuclei accepted as objects, 14 parameters
( Table 1) describing both shape and densitometric characteristics were measured. All parameters except Area—Fra
( Table 1) were dimensionless parameters calculated from
primary parameters according to algorithms in Table1.
Fig. 4. Score plot of principal components one, [t1] and three, [t3]
based on the 14 calculated image analysis parameters described in
Table 1, from nuclei in different stages of cell cycle development. (#)
Interphase, (2) prophase, ($) metaphase, (%) anaphase, (&) telophase.
Automated mitotic index analysis
1755
Fig. 5. Distance between PCA-model and 25 individual nuclei of different cell cycle stages. The PCA model was based on the 14 calculated image
analysis parameters described in Table 1 extracted from 37 interphase nuclei. Y-axis denotes distance from model in relative units. & Interphase,
q prophase, k metaphase, f anaphase, % telophase.
Relative parameters were preferred since absolute values
might vary with several external factors such as staining
intensity and illumination.
A PCA plot of principal components 1 and 3 based on
the 14 parameters measured on a set of nuclei in different
stages of mitosis and non-dividing nuclei showed that
although data to some extent were structured according
to cell cycle stage, overlapping between groups occurred
( Fig. 4). Overlapping was in fact expected, since the
transition between classes is in reality continuous and
without distinct shifts.
These observations, however, only indicated differences
in image parameters between nuclei in different stages of
the cell cycle. To test if the parameters could be used to
classify nuclei as either dividing or non-dividing, a second
PCA model was constructed based on 37 interphase nuclei
and the 14 image parameters. Twenty-five ‘new’ nuclei,
five each from interphase, prophase, metaphase, anaphase, and telophase, were thereafter presented to this
model, and the distances between individual nuclei and
the model were plotted. Interphase nuclei exhibited the
shortest distance to the model, i.e. were similar to the
model in terms of the 14 image parameters ( Fig. 5). In
fact, a critical distance could be defined which correctly
classified all 25 nuclei as either dividing or non-dividing.
Taken together, the results from multivariate modelling
indicated the potential for using the image processing,
image analysis and parameter value analysis as a method
for automatic estimation of MI.
This methodology was in the present work not
developed to a fully automated system for MI determination due to problems with autofocusing. An automatic
sequence of random image field selections, image processing and image analysis can easily be defined. However,
the automatic functioning of such a sequence requires
that either no further focusing is needed after the definition of squash coordinates, or that focusing can be done
automatically. The squash method used, yielded preparations with different focus planes for different image fields
within the squash. Autofocusing is therefore a prerequisite
for a fully automated MI determination sequence. In our
system, autofocusing was done by maximizing contrast,
i.e. the focus plane was defined as the plane with the
highest variation in grey values. However, several contrast
maximum planes were consistently detected, resulting in
poor functioning of the autofocusing and making a
completely automatic classification of nuclei impossible.
Ongoing development in the methodology of autofocusing
(Jarkrans, 1996) might solve these problems.
Acknowledgements
Ingela Lindkvist, Monica Lundström and Kjell Olofsson are
acknowledged for skilful technical assistance. This research was
supported by grants from the Swedish Council for Forestry,
Agricultural Research, and the Kempe Foundation.
References
Bengtsson E, Eriksson O, Holmquist J, Jarkrans T, Nordin B,
Stenkvist B. 1981. Segmentation of cervical cells: detection of
overlapping cell nuclei. Computer Graphics and Image
Processing 16, 382–94.
Carlsson WC, Binder WD, Fennan CD, Preisig CL. 1980.
Changes in mitotic index during onset of dormancy in
Douglas fir seedlings. Canadian Journal of Forest Research
10, 371–8.
Colombo SJ, Glerum SJ, Webb DP. 1988. Winter hardening in
first-year black spruce (Picea mariana) seedlings. Physiologia
Plantarum 76, 1–9.
Fielder P, Owens JN. 1989. A comparative study of shoot and
1756
Sunblad et al.
root development of interior and coastal Douglas fir seedlings.
Canadian Journal of Forest Research 19, 539–49.
Fox DP. 1969. Some characteristics of the cold hydrolysistechnique for staining plant tissues by the Feulgen reaction.
Journal of Histochemistry and Cytochemestry 17(4), 266–72.
Grob JA, Owens JN. 1994. Techniques to study the cell cycle
in conifer shoot apical meristems. Canadian Journal of Forest
Research 24, 472–82.
Jarkrans T. 1996. Algorithms for cell image analysis in cytology
and pathology. PhD thesis, Center for Image Analysis,
Uppsala University, Uppsala, Sweden.
O’Reilly C, Owens JN. 1987. Long-shoot bud development,
shoot growth, and foliage production in provenances of
lodgepole pine. Canadian Journal of Forest Research 17,
1421–33.
Owens JN, Molder M. 1973. A study of DNA and mitotic
activity in the vegetative apex of Douglas-fir during the
annual growth cycle. Canadian Journal of Botany 51,
1395–409.
Wold S. 1978 Cross validatory estimation of the number of
components in factor and principal component models.
Technometrics 20, 397–405.