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Proceedings Paper

Multi-class stain separation using independent component analysis
Author(s): Nicholas Trahearn; David Snead; Ian Cree; Nasir Rajpoot
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Paper Abstract

Stain separation is the process whereby a full colour histology section image is transformed into a series of single channel images, each corresponding to a given stain's expression. Many algorithms in the field of digital pathology are concerned with the expression of a single stain, thus stain separation is a key preprocessing step in these situations. We present a new versatile method of stain separation. The method uses Independent Component Analysis (ICA) to determine a set of statistically independent vectors, corresponding to the individual stain expressions. In comparison to other popular approaches, such as PCA and NNMF, we found that ICA gives a superior projection of the data with respect to each stain. In addition, we introduce a correction step to improve the initial results provided by the ICA coefficients. Many existing approaches only consider separation of two stains, with primary emphasis on Haematoxylin and Eosin. We show that our method is capable of making a good separation when there are more than two stains present. We also demonstrate our method's ability to achieve good separation on a variety of different stain types.

Paper Details

Date Published: 19 March 2015
PDF: 11 pages
Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200J (19 March 2015); doi: 10.1117/12.2081933
Show Author Affiliations
Nicholas Trahearn, The Univ. of Warwick (United Kingdom)
David Snead, Univ. Hospitals Coventry and Warwickshire NHS Trust (United Kingdom)
Ian Cree, The Univ. of Warwick Medical School (United Kingdom)
Univ. Hospitals Coventry and Warwickshire NHS Trust (United Kingdom)
Nasir Rajpoot, Qatar Univ. (Qatar)
The Univ. of Warwick (United Kingdom)


Published in SPIE Proceedings Vol. 9420:
Medical Imaging 2015: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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