Skip to main content

Research Repository

Advanced Search

Luminance adaptive biomarker detection in digital pathology images

Liu, Jingxin; Qiu, Guoping; Shen, Linlin


Jingxin Liu

Professor of Visual Information Processing

Linlin Shen


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.


Liu, J., Qiu, G., & Shen, L. (in press). Luminance adaptive biomarker detection in digital pathology images. Procedia Computer Science, 90,

Journal Article Type Article
Acceptance Date Jan 1, 2016
Online Publication Date Jul 25, 2016
Deposit Date Oct 18, 2017
Publicly Available Date Oct 18, 2017
Journal Procedia Computer Science
Print ISSN 1877-0509
Electronic ISSN 18770509
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 90
Keywords Immunohistochemistry; diaminobenzidine; image analysis; luminance; Random Forest
Public URL
Publisher URL


Luminance Adaptive Biomarker Detection in Digital Pathology Images.pdf (282 Kb)

Copyright Statement
Copyright information regarding this work can be found at the following address:

You might also like

Downloadable Citations