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Elastic and collagen fibers discriminant analysis using H&E stained hyperspectral images

Abstract

Hematoxylin and eosin (H&E) stain is one of the most common specimen staining methods in pathology diagnosis due to the capability to show the morphological structure of tissue. However, the appearance of the specific component, i.e., elastic fibers might not be recognized easily because have similar color and pattern with ones of collagen fibers. To distinguish these two components, Verhoeff’s Van Gieson (EVG) staining method is commonly used. Nevertheless, procedures of EVG stain are more complex and expensive than H&E stain. In this study, we investigate the possibility to distinguish elastic and collagen fibers from H&E stained images by applying spectral image analysis based on hyperspectral images. With experiments, we measure the transmittance spectral of 61-band H&E stained hyperspectral image, which are converted into absorbance spectral of hematoxylin, eosin, and red blood cell. As many as 3000 sampling pixels both from RGB and hyperspectral images of HE stained specimens were trained using Linear Discriminant Analysis (LDA) to get a discriminant function to classify elastic and collagen components in H&E RGB and H&E hyperspectral images. We conducted verification based on leave-one-out cross-validation of six data sets for evaluation. The verification result both visually and quantitatively compared to EVG stained image shows that the usage of hyperspectral images performs better than RGB images.

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Acknowledgements

This work is supported by Indonesia Endowment Fund for Education (LPDP) and Japan Society for The Promotion of Science (JSPS)—Indonesian Institute of Science (LIPI) Joint Research Program.

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Correspondence to Lina Septiana.

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Septiana, L., Suzuki, H., Ishikawa, M. et al. Elastic and collagen fibers discriminant analysis using H&E stained hyperspectral images. Opt Rev 26, 369–379 (2019). https://doi.org/10.1007/s10043-019-00512-8

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  • DOI: https://doi.org/10.1007/s10043-019-00512-8

Keywords

  • Pathology
  • Hyperspectral image
  • Discriminant analysis
  • Classification