Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers.
BACKGROUND
Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy.


METHODS
Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classifier is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identified using IHC and histochemistry.


RESULTS
The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesophageal cancer, colon cancer and liver cirrhosis with different colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations.


CONCLUSIONS
A robust and effective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a specified colour automatically, is easy to use and available freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html . Testing to the tool by different users showed only minor inter-observer variations in results.
College of Computer Science and Technology, North China University of Technology, Beijing, China. shujie@ncut.edu.cn.|Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, China. shujie@ncut.edu.cn.
Jie
J
Shu
University of Nottingham, Nottingham, UK.|Queens Medical Center NHS Trust (QMC), Nottingham, UK, Nottingham, UK.
G E
GE
Dolman
School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, China.
Jiang
J
Duan
School of Computer Science, University of Nottingham Ningbo Chian (UNNC), Ningbo, China.
Guoping
G
Qiu
University of Nottingham, Nottingham, UK.|Queens Medical Center NHS Trust (QMC), Nottingham, UK, Nottingham, UK.
Mohammad
M
Ilyas
D001331:Automation; D014408:Biomarkers, Tumor / Q000378:metabolism; D003116:Color; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry / Q000379:methods*; D015233:Models, Statistical
D014408:Biomarkers, Tumor
Biomarker quantification; Colour deconvolution; Colour detection; Digital pathology; Histological image processing; Software; Statistical model