Abstract
BackgroundColour 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.MethodsStatistical 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.ResultsThe 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.ConclusionsA 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.
Highlights
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
Interactive tool in imageJ We developed this colour detection method into a semi-automatic plugin in ImageJ which could be used to assist with IHC image analysis
The statistical model combined with an interactive human training process yielded better results than Colour deconvolution (CD) or CMYK methods with the DAB-stained tissue samples
Summary
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. A wide range of immunohistochemical and histochemical stains are available to assist histological assessment by providing contrast between a protein (or cell type) of interest and background. Shu et al BioMed Eng OnLine (2016) 15:46 tissue. These stains colour the target antigens or proteins, called biomarkers, with different chromogens to visualise them to assist visual microscopic analysis [1]. Diaminobenzidene (DAB) is one of the most commonly used stains in immunohistochemistry (IHC); it stains a variety of biomarkers, such as P53 and elastin dark brown. Picro-Sirius Red (PSR) is a histochemical stain commonly used to detect fibrosis in liver biopsies [4]. The connective tissue matrix is stained red by PSR whilst background liver tissue appears a pale yellow colour
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