Luminance adaptive biomarker detection in digital pathology images

Liu, Jingxin and Qiu, Guoping and Shen, Linlin (2016) Luminance adaptive biomarker detection in digital pathology images. Procedia Computer Science, 90 . pp. 113-118. ISSN 18770509

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Abstract

Digital pathology is set to revolutionise traditional approaches diagnosing and researching diseases. To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important role. Traditional methods transform the colour histopathology images into a gray scale image and apply a single threshold to separate positively stained tissues from the background. In this paper, we show that the colour distribution of the positive immunohis-tochemical stains varies with the level of luminance and that a single threshold will be impossible to separate positively stained tissues from other tissues, regardless how the colour pixels are transformed. Based on this, we propose two novel luminance adaptive biomarker detection methods. We present experimental results to show that the luminance adaptive approach significantly improves biomarker detection accuracy and that random forest based techniques have the best performances.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/799627
Keywords: Immunohistochemistry; diaminobenzidine; image analysis; luminance; Random Forest
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1016/j.procs.2016.07.032
Depositing User: LIN, Zhiren
Date Deposited: 18 Oct 2017 12:51
Last Modified: 04 May 2020 17:59
URI: http://eprints.nottingham.ac.uk/id/eprint/47281

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