Luminance adaptive biomarker detection in digital pathology images
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 hi...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/47281/ |
| _version_ | 1848797507171123200 |
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| author | Liu, Jingxin Qiu, Guoping Shen, Linlin |
| author_facet | Liu, Jingxin Qiu, Guoping Shen, Linlin |
| author_sort | Liu, Jingxin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:04:58Z |
| format | Article |
| id | nottingham-47281 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:04:58Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-472812020-05-04T17:59:49Z https://eprints.nottingham.ac.uk/47281/ Luminance adaptive biomarker detection in digital pathology images Liu, Jingxin Qiu, Guoping Shen, Linlin 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. Elsevier 2016-07-25 Article PeerReviewed Liu, Jingxin, Qiu, Guoping and Shen, Linlin (2016) Luminance adaptive biomarker detection in digital pathology images. Procedia Computer Science, 90 . pp. 113-118. ISSN 18770509 Immunohistochemistry; diaminobenzidine; image analysis; luminance; Random Forest http://www.sciencedirect.com/science/article/pii/S1877050916312121?via%3Dihub doi:10.1016/j.procs.2016.07.032 doi:10.1016/j.procs.2016.07.032 |
| spellingShingle | Immunohistochemistry; diaminobenzidine; image analysis; luminance; Random Forest Liu, Jingxin Qiu, Guoping Shen, Linlin Luminance adaptive biomarker detection in digital pathology images |
| title | Luminance adaptive biomarker detection in digital pathology images |
| title_full | Luminance adaptive biomarker detection in digital pathology images |
| title_fullStr | Luminance adaptive biomarker detection in digital pathology images |
| title_full_unstemmed | Luminance adaptive biomarker detection in digital pathology images |
| title_short | Luminance adaptive biomarker detection in digital pathology images |
| title_sort | luminance adaptive biomarker detection in digital pathology images |
| topic | Immunohistochemistry; diaminobenzidine; image analysis; luminance; Random Forest |
| url | https://eprints.nottingham.ac.uk/47281/ https://eprints.nottingham.ac.uk/47281/ https://eprints.nottingham.ac.uk/47281/ |