Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computer-based classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases – particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns.
Details
Original language | English |
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Title of host publication | Digital Pathology - 15th European Congress, ECDP 2019, Proceedings |
Editors | Constantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana |
Publisher | Springer-Verlag |
Pages | 56-64 |
Number of pages | 9 |
ISBN (print) | 9783030239367 |
Publication status | Published - 2019 |
Peer-reviewed | Yes |
Externally published | Yes |
Publication series
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11435 LNCS |
ISSN | 0302-9743 |
Conference
Title | 15th European Congress on Digital Pathology, ECDP 2019 |
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Duration | 10 - 13 April 2019 |
City | Warwick |
Country | United Kingdom |
Keywords
ASJC Scopus subject areas
Keywords
- Colour, Eosin, Hematoxylin, Histology, Texture