Luminance Adaptive Biomarker Detection in Digital Pathology Images

Jingxin Liu, Guoping Qiu*, Linlin Shen

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)113-118
Number of pages6
JournalProcedia Computer Science
Publication statusPublished - 2016
Externally publishedYes
Event20th Conference on Medical Image Understanding and Analysis, MIUA 2016 - , United Kingdom
Duration: 6 Jul 20168 Jul 2016


  • Immunohistochemistry
  • Random Forest
  • diaminobenzidine
  • image analysis
  • luminance

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