100 101 100 101
INTRODUCTION
Proliferating cell nuclear antigen (PCNA) is a
36 kD nuclear acidic protein with high expression
levels in late G1 and S cell cycle phases. Thus, an
immunohistochemical study of PCNA is an alterna-
tive to cellular proliferative analysis7.
Braz Oral Res
2004;18(2):100-4
Evaluation of the Image-Pro Plus 4.5 software for automatic
counting of labeled nuclei by PCNA immunohistochemistry
Avaliação do programa Image-Pro Plus 4.5 para contagem
automática de núcleos imunopositivos para PCNA
Jairo Silva Francisco*
Heleno Pinto de Moraes**
Eliane Pedra Dias**
ABSTRACT: The objective of this study was to create and evaluate a routine (macro) using Image-Pro Plus 4.5 soft-
ware (Media Cybernetics, Silver Spring, USA) for automatic counting of labeled nuclei by proliferating cell nuclear
antigen (PCNA) immunohistochemistry. A total of 154 digital color images were obtained from eleven sections of
reticular oral lichen planus stained by PCNA immunohistochemistry. Mean density (gray-level), red density, green
density, blue density, area, minor axis, perimeter rate and roundness were parameters used for PCNA labeled
nuclei discrimination, followed by their outlined presentation and counting in each image by the macro. Mean den-
sity and area thresholds were automatically defined based, respectively, on mean density and mean area of PCNA
labeled nuclei in the assessed image. The reference method consisted in visual counting of manually outlined
labeled nuclei. Statistical analysis of macro results versus reference countings showed a very significant correla-
tion (rs = 0.964, p < 0.001) for general results and a high level (89.8 ± 3.8%) of correctly counted labeled nuclei. We
conclude that the main parameters associated with a high correlation between macro and reference results were
mean density (gray-level) and area thresholds based on image profiles; and that Image-Pro Plus 4.5 using a rou-
tine with automatic definition of mean density and area thresholds can be considered a valid alternative to visual
counting of PCNA labeled nuclei.
DESCRIPTORS: Image processing, computer-assisted; Lichen planus; Proliferating cell nuclear antigen.
RESUMO: O objetivo deste trabalho foi desenvolver e avaliar uma macro (rotina informatizada) usando o programa
Image-Pro Plus 4.5 (Media Cybernetics, Silver Spring, EUA) para a contagem automática de núcleos imunoposi-
tivos para o antígeno nuclear em célula proliferante (PCNA). Utilizamos 154 imagens microscópicas digitalizadas
coloridas obtidas de onze cortes histológicos de líquen plano oral reticular processados por imuno-histoquímica
para PCNA. Os parâmetros densidade média (nível de cinza), densidades de vermelho, de verde e de azul, área, eixo
menor, taxa de perímetro e redondeza foram usados para a discriminação dos núcleos imunopositivos pela macro,
que, no final do processo, apresentava estes núcleos delineados e contados na imagem estudada. A definição dos
limites de corte para densidade média e área foi realizada automaticamente em função, respectivamente, da média
da densidade e da média da área dos núcleos imunopositivos presentes em cada imagem. Para controle, foi rea-
lizado o delineamento manual dos núcleos imunopositivos sobre as imagens digitalizadas e sua contagem visual.
A comparação entre os resultados das contagens da macro versus contagens do controle mostrou uma correlação
estatística significativa (rs = 0,964, p < 0,001) e uma alta proporção (89,8 ± 3,8%) de núcleos imunopositivos conta-
dos coerentemente pela macro. Concluímos que os principais parâmetros associados com a alta correlação entre os
resultados da macro e do controle foram os limites de corte para densidade média (nível de cinza) e área baseados
no padrão das imagens. Além disso, a análise de imagem usando o Image-Pro Plus 4.5 com definição automática
dos limites de corte para densidade média e área pode ser considerada uma alternativa válida para o método visual
de contagem de núcleos imunopositivos para PCNA.
DESCRITORES: Processamento de imagem assistida por computador; Líquen plano; Antígeno nuclear de célula
em proliferação.
Advantages of computer-assisted image analy-
sis for immunostaining quantification have com-
pared this method with conventional analysis
(based on microscopic observation). They include:
better reproducibility and quick results2,4,6.
* Master’s Degree Student in Buccodental Pathology; ** Adjunct Professors – Graduate Course in Pathology, School of Medicine,
Fluminense Federal University.
Oral Pathology
100 101
Francisco JS, Moraes HP, Dias EP. Evaluation of the Image-Pro Plus 4.5 software for automatic counting of labeled nuclei by PCNA
immunohistochemistry. Braz Oral Res 2004;18(2):100-4.
100 101
Konstantinidou et al.3 (1997) and Weaver,
Au9 (1997) evaluated the computer-assisted im-
age analysis to score PCNA labeled nuclei. Their
results showed a high correlation between image
analysis and visual scores (correlation coefficients:
r = 0.820, and r = 0.882, respectively). These stud-
ies concluded that computer-assisted image analy-
sis is a valid alternative for scoring PCNA immu-
nostaining in individual cells.
Image-Pro Plus 4.5 (Media Cybernetics, Silver
Spring, USA) is an image analysis software used
for quantification in several immunohistochemical
studies5. However, no study using this software for
PCNA quantitative analysis was found. Thus, the
objective of our study was to create and evaluate
a routine (macro) using Image-Pro Plus 4.5 for
automatic counting of labeled nuclei by PCNA im-
munohistochemistry.
MATERIALS AND METHODS
Eleven paraffin-embedded specimens of re-
ticular oral lichen planus registered between 1989
and 2000 at the Antônio Pedro University Hospi-
tal (Niterói, Brazil) were used. One section (5 µm
thick) from each specimen was previously pro-
cessed by PCNA immunohistochemistry by Braga1
(2001). After being placed on silane coated slides,
tissue sections were deparaffinized, rehydrated,
and submitted to heat-induced antigen retrieval
by microwaving the slides at 700 W for 9 min in
0.01 M citrate buffer pH 6.0 (Laboratory of Pathol-
ogy, Fluminense Federal University, Niterói, Bra-
zil). Endogenous peroxidase activity was blocked
by incubation for 15 min in a 3% H2O2 solution
(Laboratório ADV, São Paulo, Brazil) at room tem-
perature. Non-specific binding sites were blocked
by incubation in normal goat serum (Dako Corp.,
Carpenteria, USA) (dilution: 1/100) for 30 min.
Tissue sections were incubated with anti-PCNA
antibodies (dilution: 1/500 - PC10 code M879,
Dako Corp., Carpenteria, USA) for 30 min at 37ºC
and with Envision (Envision System, Dako Corp.,
Carpenteria, USA) for one hour at room tempera-
ture. Visualization was performed by incubation
for 5 min in 3,3’diaminobenzidine (Dako Corp.,
Carpenteria, USA). In the interval between each
step, sections were washed with distilled water
and rinsed three times in phosphate buffered sa-
line (PBS 0.01 M, pH 7.4, Laboratory of Pathology,
Fluminense Federal University, Niterói, Brazil) for
10 min. Finally, the slides were counterstained
with Mayer’s hematoxilin (Laboratory of Pathology,
Fluminense Federal University, Niterói, Brazil) for
3 min, dehydrated and coverslipped. Human tonsil
sections were used as positive staining control.
These sections were viewed through a Labo-
phot-2 optical microscope with a Plan 40 X/0.70
objective (Nikon, Tokyo, Japan). Digital images
were captured by an Iris CCD color video camera
(Sony, Tokyo, Japan) mounted on top of the mi-
croscope. This camera was connected to a 500 MHz
Pentium-III (Intel Corp., Austin, USA) personal
computer by a 4 MB PCI All-in-Wonder Pro image
card (ATI Technologies, Santa Clara, USA). The
three color channels of the camera were balanced
by adjusting the microscope light intensity and
camera gain and offset, using an ATI image card
software. Images were viewed in a 15” Syncmaster
500S color monitor (Samsung Corp., Kyungki-Do,
Korea).
A total of 154 images (fourteen from each sec-
tion) were digitized and stored in uncompressed tiff
format (tagged image file format) with 24-bit RGB
class and 640 x 480 pixel resolution.
The nuclei were considered positive for PCNA
labeling if their immunostains were equal or larger
than 50 percent of the nuclear area. Weak brown
stains were excluded from the counting.
Based on their visual identification, the PCNA
positive nuclei in all images were outlined in green
using Photoshop 6.0 (Adobe Systems Incorporated,
San Jose, USA). These images were named “ref-
erence images” (Figure 1). The nuclei outlined in
green were counted image per image, and reference
countings were defined.
The macro for counting PCNA immunostained
nuclei in the stored images was developed using
Image-Pro Plus 4.5. The labeled nuclei image seg-
mentation was based on RGB 8-bit per channel pa-
rameters: red (100-210), green (85-190), and blue
(80-180). The segmented areas in the images were
filtered to count PCNA labeled nuclei with 50% or
more of their nuclear area with strong or moderate
immunostaining. This filtering used thresholds
as follows: mean density (minimum = 115; maxi-
mum = 164-169, proportionally for labeling mean
density per image), red density (125-185), green
density (110-172), blue density (110-172), area
(minimum = labeled nuclear mean area per im-
age/2.3), axis (minimum = 2 µm), roundness (0.6-
1.0), and perimeter ratio (0.5-1.0). Mean density
and area thresholds were automatically defined
based, respectively, on mean density and mean
area of PCNA labeled nuclei in the assessed im-
age. After macro application, the PCNA labeled
Francisco JS, Moraes HP, Dias EP. Evaluation of the Image-Pro Plus 4.5 software for automatic counting of labeled nuclei by PCNA
immunohistochemistry. Braz Oral Res 2004;18(2):100-4.
102 103 102 103
were analyzed with SPSS 10.0 statistical software
(SPSS Inc., Chicago, USA).
RESULTS
Results of reference and automatic countings
are presented in Table 1. Detailed countings of
nuclei were viewed outlined with yellow in the im-
ages (named “macro images” - Figure 2) and their
counting per image was obtained.
Comparing the 154 macro images with their
respective reference images, the scores of PCNA
labeled nuclei, correctly and incorrectly counted by
the automatic routine, were defined. These results
TABLE 1 - Counting results of PCNA immunohistochemistry labeled nuclei in the 154 studied images (fourteen per
case).
Case Reference* Macro Coherent Included Excluded Overmarked
A 115 100.0% 124 107.8% 97 84.3% 27 23.5% 16 13.9% 2 1.8%
B 371 100.0% 413 111.3% 347 93.5% 66 17.8% 14 3.8% 10 2.7%
C 269 100.0% 288 107.1% 251 93.3% 37 13.8% 9 3.4% 9 3.3%
D 83 100.0% 102 122.9% 78 94.4% 24 28.9% 5 6.0% 0 0.0%
E 102 100.0% 96 94.1% 87 85.3% 9 8.8% 15 14.7% 0 0.0%
F 114 100.0% 113 99.1% 101 88.6% 12 10.5% 12 10.5% 1 0.9%
G 158 100.0% 157 99.4% 137 86.7% 20 12.7% 18 11.4% 3 1.9%
H 385 100.0% 366 95.1% 334 86.8% 32 8.3% 29 7.5% 22 5.7%
I 336 100.0% 349 103.9% 311 92.6% 38 11.3% 16 4.7% 9 2.7%
J 190 100.0% 216 113.7% 180 94.7% 36 19.0% 10 5.3% 0 0.0%
K 335 100.0% 321 95.9% 295 88.1% 26 7.8% 22 6.5% 18 5.4%
Total of nuclei 2458 - 2545 - 2218 - 327 - 166 - 74 -
Mean percentage - 100.0% - 104.6% - 89.8% - 14.8% - 8.0% - 2.2%
Standard deviation - - - 9.0% - 3.8% - 6.8% - 4.0% - 2.0%
*All percentage values were related to the reference (visual) countings.
FIGURE 1 - Reference image: the PCNA positive nuclei
in all images were manually outlined based on visual
identification (immunohistochemistry for PCNA, oral
lichen planus).
FIGURE 2 - Macro image: the PCNA labeled nuclei were
automatically outlined and counted by the macro (im-
munohistochemistry for PCNA, oral lichen planus).
102 103
Francisco JS, Moraes HP, Dias EP. Evaluation of the Image-Pro Plus 4.5 software for automatic counting of labeled nuclei by PCNA
immunohistochemistry. Braz Oral Res 2004;18(2):100-4.
102 103
PCNA labeled nuclei were obtained by compar-
ing reference versus macro respective images:
the PCNA labeled nuclei correctly counted by the
macro (coherent), those incorrectly included by the
macro (included), those excluded by the macro (ex-
cluded), and those counted together (overmarked).
Percentage values are related to reference (visual)
countings. Graph 1 shows the correlation between
counting values for 154 macro images versus ref-
erence images. Both counting methods presented
a very high Spearman nonparametric correlation
coefficient (rs = 0.964, p < 0.001, bi-tailed).
DISCUSSION
Image-Pro Plus 4.5, in our opinion, has limita-
tions and tools compatible with other image analy-
sis software. Moreover, the parameter values ap-
plied to this study can be useful in other studies
on PCNA immunostaining image analysis.
The weak or small PCNA immunostains were
not counted because of their occurrence in non-
proliferating cells7. Therefore, the quiescent cell
labeled nuclei with low PCNA expression levels
had higher chances of being excluded from the
counting.
Weaver, Au9 (1997) obtained a high correlation
coefficient (r = 0.882) when they compared image
analysis [using size (area), gray-level (mean den-
sity) and proportion of labeled nuclear area] and
visual counting of PCNA labeled nuclei in head and
neck, and bladder tumors. Using the same mate-
rial, Weaver, Au8 (1997) assessed the automatic
threshold definition applicability, and concluded
that the automatic gray-level (mean density) and
hue thresholds improved the accuracy and the
reproducibility of image analysis results.
The mean density and area thresholds in
Image-Pro Plus 4.5 macro were the most impor-
tant parameters used in PCNA labeled nuclei dis-
crimination. This profile is similar to the PCNA
immunostaining image analysis performed by
Konstantinidou et al.3 (1997). The macro auxil-
iary parameters were as follows: red density, green
density, blue density, minor axis, perimeter rate
and roundness. The perimeter rate and roundness
parameters were also used by Konstantinidou et
al.3 (1997).
The assessed material showed a variation in
the PCNA labeling intensity in all cases. This pro-
file was incompatible with the use of fixed thresh-
olds for labeled nuclei mean density. However, if
cut limits are placed in an adequate manner for the
intermediary intensity pattern, images with greater
intensity marks would tend to include inadequate
nuclei in the count. The opposite happens with
images of less relative intensity. To avoid this,
Konstantinidou et al.3 (1997) used an interactive
introduction of the mean density thresholds re-
lated to the brown labeling intensity, and Weaver,
Au8 (1997) used a preliminary sub-routine for the
automatic definition of the mean density thresh-
olds in the image analysis for PCNA immunohis-
tochemistry.
We also applied automatic selection of the
mean density thresholds. In order to make this
possible, the program analyzed the mean density
of the PCNA labeled nuclei in each studied image
so that, in the sequence, the mean density thresh-
old for counting the acceptable labeled nuclei was
defined.
A significant variation in the area of PCNA
nuclear immunostains was observed in the stud-
ied sections. To select the minimal area threshold,
other authors used the interactive definition of
minor value for acceptable immunostained areas
in the images. The minimal area threshold in our
study was empirically defined using the mean area
of immunostains per image divided by 2.3 (this
value was obtained based on preliminary analysis
of the assessed images – data not shown).
The automatic routine we used has a greater
potential of reproducibility because of routine runs
with only “one button command”, which accom-
plishes the definition of PCNA immunostaining
P
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ei
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ou
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-
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ac
ro
All cases (154 images)
PCNA labeled nuclei countings - reference
60
50
40
30
20
10
0
0 10 20 30 40 50 60
GRAPH 1 - Diagrammatic presentation of the correla-
tion between macro and reference countings of PCNA
labeled nuclei for the 154 images (Spearman correla-
tion coefficient rs = 0.964, p < 0.001, bi-tailed).
Francisco JS, Moraes HP, Dias EP. Evaluation of the Image-Pro Plus 4.5 software for automatic counting of labeled nuclei by PCNA
immunohistochemistry. Braz Oral Res 2004;18(2):100-4.
104 PB
area and mean density thresholds resulting in an
instantaneous positive nuclei counting per im-
age.
The statistical analysis among macro count-
ing results versus reference countings showed a
high correlation coefficient (rs = 0.964, p < 0.001).
This means that there is an underlying propor-
tion among both countings. There was a high level
of PCNA labeled nuclei correctly counted by the
macro (Table 1, coherent = 89.8 ± 3.8%), indicating
good sensitivity (proportion of the acceptable PCNA
labeled nuclei that were recognized by the macro).
These findings are associated with the automatic
reproducibility potential and support the macro
practical applicability.
CONCLUSION
The main parameters associated with a high
correlation between macro and reference results
were mean density (gray-level) and area thresholds
based on image profiles.
Image-Pro Plus 4.5, using a routine with auto-
matic definition of mean density and area thresh-
olds, can be considered a valid alternative to visual
counting of PCNA labeled nuclei.
REFERENCES
1. Braga BRS. Análise da atividade proliferativa no líquen
plano oral [Dissertação de Mestrado]. Niterói: Universidade
Federal Fluminense; 2001.
2. Francis IM, Adeyanju MO, George SS, Junaid TA, Luthra
UK. Manual versus image analysis estimation of PCNA in
breast carcinoma. Anal Quant Cytol Histol 2000;22:11-
6.
3. Konstantinidou A, Patsouris E, Kavantzas N, Pavlopoulos
PM, Bouropoulou V, Davaris P. Computerized determi-
nation of proliferating cell nuclear antigen expression in
meningiomas. A comparison with non-automated method.
Gen Diagn Pathol 1997;142:311-6.
4. Lin HC, Sotnikov AV, Fosdick L, Bostick RM, Willett WC.
Quantification of proliferating cell nuclear antigen in large
intestinal crypt by computer-assisted image analysis. Can-
cer Epidemiol Biomarkers Prev 1996;5:109-14.
5. Media Cybernetics. Image-Pro Plus - application notes.
Silver Spring: Media Cybernetics; 2002. Available from:
URL: http://www.mediacy.com/action.htm.
6. Seidal T, Balaton AJ, Battifora H. Interpretation and quanti-
fication of immunostains. Am J Surg Pathol 2001;25:1204-
7.
7. Somanathan S, Suchyna TM, Siegel AJ, Berezney R. Target-
ing of PCNA to sites of DNA replication in the mammalian
cell nucleus. J Cell Biochem 2001;81:56-67.
8. Weaver JR, Au JL. Application of automatic thresholding
in image analysis scoring of cells in human solid tumors
labeled for proliferation markers. Cytometry 1997;29:128-
35.
9. Weaver JR, Au JL. Comparative scoring by visual and im-
age analysis of cells in human solid tumors labeled for
proliferation markers. Cytometry 1997;27:189-99.
Received for publication on Jun 30, 2003
Sent for alterations on Aug 19, 2003
Accepted for publication on Apr 05, 2004
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