Quantitative image analysis of immunohistochemical stains using a CMYK color model
© Pham et al; licensee BioMed Central Ltd. 2007
Received: 01 February 2007
Accepted: 27 February 2007
Published: 27 February 2007
Computer image analysis techniques have decreased effects of observer biases, and increased the sensitivity and the throughput of immunohistochemistry (IHC) as a tissue-based procedure for the evaluation of diseases.
We adapted a Cyan/Magenta/Yellow/Key (CMYK) model for automated computer image analysis to quantify IHC stains in hematoxylin counterstained histological sections.
The spectral characteristics of the chromogens AEC, DAB and NovaRed as well as the counterstain hematoxylin were first determined using CMYK, Red/Green/Blue (RGB), normalized RGB and Hue/Saturation/Lightness (HSL) color models. The contrast of chromogen intensities on a 0–255 scale (24-bit image file) as well as compared to the hematoxylin counterstain was greatest using the Yellow channel of a CMYK color model, suggesting an improved sensitivity for IHC evaluation compared to other color models. An increase in activated STAT3 levels due to growth factor stimulation, quantified using the Yellow channel image analysis was associated with an increase detected by Western blotting. Two clinical image data sets were used to compare the Yellow channel automated method with observer-dependent methods. First, a quantification of DAB-labeled carbonic anhydrase IX hypoxia marker in 414 sections obtained from 138 biopsies of cervical carcinoma showed strong association between Yellow channel and positive color selection results. Second, a linear relationship was also demonstrated between Yellow intensity and visual scoring for NovaRed-labeled epidermal growth factor receptor in 256 non-small cell lung cancer biopsies.
The Yellow channel image analysis method based on a CMYK color model is independent of observer biases for threshold and positive color selection, applicable to different chromogens, tolerant of hematoxylin, sensitive to small changes in IHC intensity and is applicable to simple automation procedures. These characteristics are advantageous for both basic as well as clinical research in an unbiased, reproducible and high throughput evaluation of IHC intensity.
Immunohistochemistry (IHC) for the evaluation of antigen expression as well as higher resolution methodologies for cytogenetic analysis are standard procedures for the diagnosis and prognosis of cancer and other diseases . In recent years, cancer treatment response and disease progression increasingly rely on IHC to monitor changes in targeted antigens. Abundances of antigens have so far relied primarily on visual scoring and to a lesser extent computer-assisted image processing techniques . A major advantage of computer techniques is the avoidance of inter-observer variability in interpreting subtle antigen level changes . In addition, pattern recognition, which has been applied to image analysis of fluorescence in situ hybridization , may also be applied to IHC image analysis of compartmentalized antigen distributions, particularly with recent developments in self-learning computer programs . Most computer-based techniques for IHC image analysis have so far had limited applicability due to several drawbacks including a need for specific software systems, often with considerable need for user input [6–9]. These image analyses have commonly been performed on the single assessment of 3,3'-diaminobenzidine (DAB) labeling for a variety of cytoplasmic markers. Consequently, misclassifications were frequently encountered when two or more chromogens with overlapping absorption spectra were used simultaneously . Specialized color deconvolution algorithms can be applied to discriminate multiple spectra . Alternatively, the recent application of spectral imaging offers an optimal method to capture and analyze images at multiple wavelengths . However, digitally captured IHC brightfield images are usually stored as composites of three 8-bit monochromatic red, green and blue channels (RGB), which can be converted to the Cyan/Magenta/Yellow/Key (CMYK) or Hue/Saturation/Lightness (HSL) color space. Previously, components of the RGB , their normalization (nR, nG, nB; nRGB) , or HSL [5, 8, 13] have been applied towards IHC quantification.
In this study, we adapted a CMYK color model as a simple method to quantify IHC staining with three commonly used chromogens including 3-amino-9-ethylcarbazole (AEC), DAB, and NovaRed with hematoxylin counterstain. For simplicity, images of color bars representing IHC staining were used to characterize the performance of individual channels in the color models CMYK, RGB, nRGB and HSL. These results indicated advantages with the Yellow channel analysis, based on a CMYK color model. Therefore, the Yellow channel method was further tested on three applications. First, sensitivities of image analysis methods were compared in the IHC staining of phosphorylated signal transduction and transcription factor-3 (p-STAT3) in tumor xenografts. Following, the performance of a high throughput automated Yellow channel method was compared to observer-dependent methods, including positive color selection for a hypoxia marker carbonic anhydrase-IX (CA-IX)-labeled DAB and visual scoring for epidermal growth factor receptor (EGFR)-labeled NovaRed in the second and third applications, respectively.
Image acquisition and processing
Slides were scanned with a ScanScope CS (Aperio Technologies, Vista, CA) using brightfield imaging at 20× magnification. Specimen areas were selected and individual images were saved in a 24-bit RGB TIFF file format with a resolution of 1 μm/pixel using the ImageScope software (Aperio Technologies). The automated analysis of the TIFF image files was performed using the programming language IDL 6.3 (ITT Visual Information Solutions, Boulder, Colorado) [14, 15] for IHC quantification using RGB , normalized RGB , HSL [8, 13], and CMYK as described below.
CMYK image analysis method
We adapted a CMYK model with maximum grey component replacement, a criterion in which the lowest brightness level is subtracted from all channels. This criterion effectively discriminates color differences by subtracting out the grey level, a level of equal intensity among the three channels (C, M, Y). Consequently, this subtraction sets one of the color channels to zero in each image. The selection of this display criterion for an image using a popular software Adobe® Photoshop®7.0, involves the following sequence of menu selections: Edit, Color settings, Custom CMYK and Black generation set at Maximum. In this study, CMYK values were derived from the CMYK_Convert library procedure in IDL 6.3 using the equations:
(* indicates multiplication)
K (black) = 255 - maximum(R, G, B)
C = 255* [1 - R/(255 - K)] (if K = 255 then C = 0)
M = 255* [1 - G/(255 - K)] (if K = 255 then M = 0)
Y = 255* [1 - B/(255 - K)] (if K = 255 then Y = 0)
Mean intensity measurements in regions of interest were computed without the use of positive color intensity thresholds. However, proportions of labeled area in tissue were determined with a positive range intensity of 21+ and 93+ for the Yellow and nRed channels, respectively.
Spectral characteristics of AEC, DAB and NovaRed were displayed with the different color models using color bars that were generated from a range of representative IHC staining derived from the xenograft studies described below, as well as additional IHC archived slides that showed a greater range of staining intensity.
Changes in STAT3 phosphorylation (p-STAT3) levels were associated with different conditions, including growth factor stimulation and sampling techniques as previously described . Formalin-fixed paraffin embedded specimens were obtained to compare IHC staining using different chromogens AEC, DAB or NovaRed, and hematoxylin counterstain. Briefly, a tumor xenograft was excised and divided into two equal parts. One part was further divided and subjected to immediate formalin fixation, or protein extraction for Western blot analysis . The second part was incubated with 50 ng/ml epidermal growth factor (EGF, Sigma-Aldrich, Oakville, ON) in PBS for 20 minutes prior to specimen analysis. The antibody S727p-STAT3 (BD Biosciences, Mississauga, ON) was used for Western blotting at a dilution of 1:100, and anti-GAPDH used at a dilution of 1:5000 (Ambion, Foster City, CA). Protein bands were detected with ECL plus fluorescence, imaged with a Typhoon system and their intensities were quantified (mean intensity × band area) and normalized with GAPDH using ImageQuant5.2 software (GE Healthcare, Little Chalfont, Buckinghamshire, UK).
Second, p-STAT3 levels were also quantified in formalin-fixed paraffin embedded sections of fine-needle samples obtained from a tumor xenograft. These samples were subjected to either immediate or delayed fixation after 5, 20 or 60 minutes, which caused an increase in p-STAT3.
Clinical test sets for comparison with CMYK model
Image analysis was performed on two clinical image data sets collected for other histopathological studies. The first study comprised of a total of 414 sections obtained from three adjacent sections of 138 frozen uterine cervical carcinoma biopsies. Carbonic anhydrase-IX-labeled DAB was previously evaluated with a positive color selection method . In brief, a visual selection of a range of positive brown color pixels was performed using Adobe®Photoshop® 7.0 and batched analyzed using the IDL 6.3 programming language. Image analysis was performed in areas designated by tumor masks.
The second study comprised of a total of 282 formalin-fixed and paraffin embedded NSCLC biopsies arranged in 8 tissue microarrays which were IHC-stained for EGFR-labeled NovaRed . Three cores of 0.6 mm diameter represented each case with evaluation completed on cases represented by at least 2 cores (n = 256). Two evaluators visually scored with a scale 0–3 (0 for absence of membrane staining in cancer cells) and their scores were strongly associated (r = 0.97). Thus, the mean of core scores was used as the final score for individual cases. Image analysis was performed in areas designated by whole tissue core masks since anti-EGFR demonstrated high specificity for cancer cells.
Specimens were processed with routine protocols using anti-S727p-STAT3 (BD Biosciences), anti-CA-IX (a generous gift of Dr. Adrian Harris, University of Oxford, UK)  and anti-EGFR (clone 31G7, Zymed Technologies, Invitrogen, Burlington, ON). The Idetect Ultra HRP Detection System (ID Labs Inc, London, Ontario) was used to visualize either DAB (Dako Corp, Carpinteria, CA), NovaRED or AEC (Vector Laboratories). Counterstaining was performed with Gill modified hematoxylin (Harleco®).
IHC spectral characteristics
Unique features of the presented Yellow channel included an automatic setting at zero for the hematoxylin counterstain as well as a greater utilization of the 0–255 intensity scale compared to nR or nB. These characteristics demonstrated that the Yellow channel achieved a high contrast between different chromogen intensities as well as between the tested chromogens and the hematoxylin counterstain. The performance of the CMYK Yellow channel method was further tested in three applications which used other automated and observer-dependent methods.
Activation of STAT3
EGF Stimulation (%)
Positive color selection analysis compared to CMYK
A comparison of image analysis methods
% Positive area
% Positive area
Positive color selection (% positive area)
Pathological visual scoring compared to CMYK
Levels of IHC stains are increasingly used to monitor biological disease markers during cancer treatment and disease progression. This image analysis study tested the use of the Yellow channel of a CMYK color model for IHC quantification. Although the programming language IDL 6.3 was used to automate the analysis process here, the availability of the CMYK model with maximum grey component replacement function in popular image processing software packages makes this method readily accessible.
Spectral characterization of the chromogens AEC, DAB and NovaRed in the absence of the hematoxylin counterstain showed a relationship with at least one channel of all color models, consistent with the RGB and HSL results in previous reports [6, 8, 12]. However, in the presence of the hematoxylin counterstain the Yellow channel analysis method applied here achieved the best contrast on a 0–255 scale, between chromogen and hematoxylin as well as for different chromogen intensities. These characteristics of the Yellow channel resulted in a higher sensitivity towards detecting subtle IHC intensity changes relevant to markers associated with treatment response, prognosis and pathobiology of cancers in the examples presented in this paper.
The least amount of observer-dependent input was applied to the image analysis process. Analyses were performed in regions of interest with the omission of obvious dark artifacts which may occur due to mixing with endogenous pigments, debris, tissue drying and other method-dependent variables as recognized in such tissue processing techniques [2, 20]. Although a comprise on the intensity scale occurs when the IHC stain is near black as presented in the results, the saturated chromogen intensities remained within the higher levels of the intensity scale and therefore was likely negligible. Alternatively, independent of intensity levels, measurements were also evaluated based on proportions of labeled area, but it is dependent on observer selection of positive intensity ranges.
Antigen expression in tissue sections can be expressed as either the mean staining intensity or the stained area fraction. The mean staining intensity measurements described in this paper show changes in relative antigen expression when these were ubiquitously expressed, or equally distributed in compartment(s) of interest. These antigen characteristics suggest that other frequently IHC-labeled antigens such as proliferation and apoptosis markers can also be quantified using image analysis methods such as Yellow channel in addition to conventional estimates of their presence or absence. These markers have been informative for the further sub-classification of cancer stages identified using standard histology [21, 22]. However, in scenarios where differences in cellular distributions are important for disease profiling  recent developments in pattern recognition programs are possible solutions .
The analysis of different IHC applications here demonstrated that the Yellow-CMYK channel method provides consistent results as well as higher performance for IHC quantification compared to other automated and manual techniques. The Yellow channel has several advantages, including its applicability to different chromogens, tolerance of hematoxylin, greater utilization of the intensity scale and readiness for automation. In particular, the mean Yellow intensity measurements are independent of arbitrary threshold selection. These advantages are important to IHC-based analysis in basic as well as clinical research  where the biological changes as well as methodological variances need to be quantified in an unbiased, reproducible and high throughput manner.
We would like to thank James Jonkman and Trudey Nicklee for insightful image analysis discussions and Tim Richardson for the introduction to color spaces. Nhu-An Pham holds a Graduate Scholarships Doctoral Award from the Canada Institutes of Health Research. The work is supported by the grants from the Canadian Cancer Society, The Terry Fox Foundation and Canadian Institutes of Health Research (MOP-49585 to MST).
- Anderson JE, Hansen LL, Mooren FC, Post M, Hug H, Zuse A, Los M: Methods and biomarkers for the diagnosis and prognosis of cancer and other diseases: towards personalized medicine. Drug Resist Updat. 2006, 9 (4-5): 198-210. 10.1016/j.drup.2006.08.001.View ArticlePubMed
- Walker RA: Quantification of immunohistochemistry-issues concerning methods, utility and semiquantitative assessment I. Histopathology. 2006, 49 (4): 406-410. 10.1111/j.1365-2559.2006.02514.x.View ArticlePubMed
- Hatanaka Y, Hashizume K, Kamihara Y, Itoh H, Tsuda H, Osamura RY, Tani Y: Quantitative immunohistochemical evaluation of HER2/neu expression with HercepTestTM in breast carcinoma by image analysis. Pathol Int. 2001, 51 (1): 33-36. 10.1046/j.1440-1827.2001.01162.x.View ArticlePubMed
- Langer S, Kraus J, Jentsch I, Speicher MR: Multicolor chromosome painting in diagnostic and research applications. Chromosome Res. 2004, 12 (1): 15-23. 10.1023/B:CHRO.0000009326.21752.88.View ArticlePubMed
- Kayser G, Radziszowski D, Bzdyl P, Sommer R, Kayser K: Theory and implementation of an electronic, automated measurement system for images obtained from immunohistochemically stained slides. Anal Quant Cytol Histol. 2006, 28 (1): 27-38.PubMed
- Brey EM, Lalani Z, Johnston C, Wong M, McIntire LV, Duke PJ, Patrick CW: Automated selection of DAB-labeled tissue for immunohistochemical quantification. J Histochem Cytochem. 2003, 51 (5): 575-584.View ArticlePubMed
- Goedkoop AY, de Rie MA, Teunissen MB, Picavet DI, van der Hall PO, Bos JD, Tak PP, Kraan MC: Digital image analysis for the evaluation of the inflammatory infiltrate in psoriasis. Arch Dermatol Res. 2005, 297 (2): 51-59. 10.1007/s00403-005-0578-4.View ArticlePubMed
- Kim D, Gregory CW, Smith GJ, Mohler JL: Immunohistochemical quantitation of androgen receptor expression using color video image analysis. Cytometry. 1999, 35 (1): 2-10. 10.1002/(SICI)1097-0320(19990101)35:1<2::AID-CYTO2>3.0.CO;2-Y.View ArticlePubMed
- Matkowskyj KA, Cox R, Jensen RT, Benya RV: Quantitative immunohistochemistry by measuring cumulative signal strength accurately measures receptor number. J Histochem Cytochem. 2003, 51 (2): 205-214.View ArticlePubMed
- Ruifrok AC, Katz RL, Johnston DA: Comparison of quantification of histochemical staining by hue-saturation-intensity (HSI) transformation and color-deconvolution. Appl Immunohistochem Mol Morphol. 2003, 11 (1): 85-91. 10.1097/00022744-200303000-00014.PubMed
- Taylor CR, Levenson RM: Quantification of immunohistochemistry-issues concerning methods, utility and semiquantitative assessment II. Histopathology. 2006, 49 (4): 411-424. 10.1111/j.1365-2559.2006.02513.x.View ArticlePubMed
- Leal S, Diniz C, Sa C, Goncalves J, Soares AS, Rocha-Pereira C, Fresco P: Semiautomated computer-assisted image analysis to quantify 3,3'-diaminobenzidine tetrahydrochloride-immunostained small tissues. Anal Biochem. 2006, 357 (1): 137-143. 10.1016/j.ab.2006.07.017.View ArticlePubMed
- Lehr HA, van der Loos CM, Teeling P, Gown AM: Complete chromogen separation and analysis in double immunohistochemical stains using Photoshop-based image analysis. J Histochem Cytochem. 1999, 47 (1): 119-126.View ArticlePubMed
- Hedley D, Pintilie M, Woo J, Nicklee T, Morrison A, Birle D, Fyles A, Milosevic M, Hill R: Up-regulation of the redox mediators thioredoxin and apurinic/apyrimidinic excision (APE)/Ref-1 in hypoxic microregions of invasive cervical carcinomas, mapped using multispectral, wide-field fluorescence image analysis. Am J Pathol. 2004, 164 (2): 557-565.PubMed CentralView ArticlePubMed
- Hedley D, Pintilie M, Woo J, Morrison A, Birle D, Fyles A, Milosevic M, Hill R: Carbonic anhydrase IX expression, hypoxia, and prognosis in patients with uterine cervical carcinomas. Clin Cancer Res. 2003, 9 (15): 5666-5674.PubMed
- Schwock J, Ho JC, Luther E, Hedley D, Geddie WR: Detection and measurement of phosphorylated signaling proteins in solid tumor fine-needle biopsies by laser-scanning cytometry. Diagostic Molecular Pathology.
- Pham NA, Jacobberger JW, Schimmer AD, Cao P, Gronda M, Hedley DW: The dietary isothiocyanate sulforaphane targets pathways of apoptosis, cell cycle arrest, and oxidative stress in human pancreatic cancer cells and inhibits tumor growth in severe combined immunodeficient mice. Mol Cancer Ther. 2004, 3 (10): 1239-1248.PubMed
- Iakovlev V, Morrison A, Pintile M, Hill R, Hedley D: Quantitative assessment of heterogeneously expressed markers within histological sections. 57th Annual Meeting of the Canadian Association of Pathologists 2006, St. John, NL, Canada. 2006
- Aviel-Ronen S, Ling C, Lu M, Lau D, Ho JC, Liu N, Tsao MS: p16 methylation and protein expression in non-small cell lung cancer: September 16-21 2006; Montreal, Quebec Canada.2006, , Modern Pathology 19: supplement 3:
- Seidal T, Balaton AJ, Battifora H: Interpretation and quantification of immunostains. Am J Surg Pathol. 2001, 25 (9): 1204-1207. 10.1097/00000478-200109000-00013.View ArticlePubMed
- Kapur P, Rakheja D, Balani JP, Roy LC, Amirkhan RH, Hoang MP: Phosphorylated histone H3, Ki-67, p21, fatty acid synthase, and cleaved caspase-3 expression in benign and atypical granular cell tumors. Arch Pathol Lab Med. 2007, 131 (1): 57-64.PubMed
- Matos I, Dufloth R, Alvarenga M, Zeferino LC, Schmitt F: p63, cytokeratin 5, and P-cadherin: three molecular markers to distinguish basal phenotype in breast carcinomas. Virchows Arch. 2005, 447 (4): 688-694. 10.1007/s00428-005-0010-7.View ArticlePubMed
- Kay EW, Walsh CJ, Cassidy M, Curran B, Leader M: C-erbB-2 immunostaining: problems with interpretation. J Clin Pathol. 1994, 47 (9): 816-822.PubMed CentralView ArticlePubMed
- Fitzgibbons PL, Murphy DA, Dorfman DM, Roche PC, Tubbs RR: Interlaboratory comparison of immunohistochemical testing for HER2: results of the 2004 and 2005 College of American Pathologists HER2 Immunohistochemistry Tissue Microarray Survey. Arch Pathol Lab Med. 2006, 130 (10): 1440-1445.PubMed
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