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Shu, Jie, Dolman, G.E., Duan, Jiang, Qiu, Guoping and Ilyas, Mohammad (2016) Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers. BioMedical Engineering OnLine, 15 (1). p. 46. ISSN 1475-925X

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Abstract

Background: Colour is the most important feature used in quantitative immunohisto- chemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to con rm 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 rst 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 classi- er 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 identi ed 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 oesopha- geal cancer, colon cancer and liver cirrhosis with di erent 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 e ective 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 speci ed colour automatically, is easy to use and avail- able freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html. Testing to the tool by di erent users showed only minor inter-observer variations in results.

Item Type:	Article
RIS ID:	https://nottingham-repository.worktribe.com/output/783605
Keywords:	Colour detection; Statistical model; Colour deconvolution; Digital pathology; Histological image processing; Biomarker quantification; Software
Schools/Departments:	University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number:	10.1186/s12938-016-0161-6
Depositing User:	LIN, Zhiren
Date Deposited:	19 Oct 2017 10:02
Last Modified:	04 May 2020 17:44
URI:	https://eprints.nottingham.ac.uk/id/eprint/47327

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