Multi-class stain separation using independent component analysis

Nicholas Trahearn; David Snead; Ian Cree; Nasir Rajpoot

doi:10.1117/12.2081933

19 March 2015 Multi-class stain separation using independent component analysis

Nicholas Trahearn, David Snead, Ian Cree, Nasir Rajpoot

Proceedings Volume 9420, Medical Imaging 2015: Digital Pathology; 94200J (2015) https://doi.org/10.1117/12.2081933
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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.

Citation Download Citation

Nicholas Trahearn, David Snead, Ian Cree, and Nasir Rajpoot "Multi-class stain separation using independent component analysis", Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200J (19 March 2015); https://doi.org/10.1117/12.2081933

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