Journal Article
. 2013 Jun; 4(Suppl):S11.
doi: 10.4103/2153-3539.109869.

Histological stain evaluation for machine learning applications

Jimmy C Azar 1 Christer Busch  Ingrid B Carlbom  
Affiliations
  • PMID: 23766933
  •     3 References
  •     3 citations

Abstract

Aims: A methodology for quantitative comparison of histological stains based on their classification and clustering performance, which may facilitate the choice of histological stains for automatic pattern and image analysis.

Background: Machine learning and image analysis are becoming increasingly important in pathology applications for automatic analysis of histological tissue samples. Pathologists rely on multiple, contrasting stains to analyze tissue samples, but histological stains are developed for visual analysis and are not always ideal for automatic analysis.

Materials And Methods: Thirteen different histological stains were used to stain adjacent prostate tissue sections from radical prostatectomies. We evaluate the stains for both supervised and unsupervised classification of stain/tissue combinations. For supervised classification we measure the error rate of nonlinear support vector machines, and for unsupervised classification we use the Rand index and the F-measure to assess the clustering results of a Gaussian mixture model based on expectation-maximization. Finally, we investigate class separability measures based on scatter criteria.

Results: A methodology for quantitative evaluation of histological stains in terms of their classification and clustering efficacy that aims at improving segmentation and color decomposition. We demonstrate that for a specific tissue type, certain stains perform consistently better than others according to objective error criteria.

Conclusions: The choice of histological stain for automatic analysis must be based on its classification and clustering performance, which are indicators of the performance of automatic segmentation of tissue into morphological components, which in turn may be the basis for diagnosis.

Keywords: F-measure; Fisher criterion; Gaussian mixture model; Mahalanobis distance; Rand index; Support vector machines; expectation-maximization; high throughput imaging systems.

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