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Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers

Overview of attention for article published in BioMedical Engineering OnLine, April 2016
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Title
Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers
Published in
BioMedical Engineering OnLine, April 2016
DOI 10.1186/s12938-016-0161-6
Pubmed ID
Authors

Jie Shu, G. E. Dolman, Jiang Duan, Guoping Qiu, Mohammad Ilyas

Abstract

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. 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. 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. 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 26%
Student > Master 10 15%
Student > Bachelor 7 11%
Researcher 6 9%
Student > Doctoral Student 4 6%
Other 8 12%
Unknown 13 20%
Readers by discipline Count As %
Medicine and Dentistry 11 17%
Agricultural and Biological Sciences 8 12%
Biochemistry, Genetics and Molecular Biology 8 12%
Computer Science 4 6%
Neuroscience 4 6%
Other 12 18%
Unknown 18 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 May 2016.
All research outputs
#20,656,820
of 25,374,917 outputs
Outputs from BioMedical Engineering OnLine
#607
of 867 outputs
Outputs of similar age
#232,658
of 312,590 outputs
Outputs of similar age from BioMedical Engineering OnLine
#8
of 11 outputs
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