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Analytical Cellular Pathology
Volume 22 (2001), Issue 3, Pages 133-142

Spectral Imaging of Multi-Color Chromogenic Dyes in Pathological Specimens

Merryn V. E. Macville,1,2 Jeroen A. W. M. Van der Laak,1 Ernst J. M. Speel,3 Nir Katzir,4 Yuval Garini,4 Dirk Soenksen,5 George McNamara,6 Peter C. M. de Wilde,1 Antonius G. J. M. Hanselaar,1 Anton H.N. Hopman,3 and Thomas Ried2

1Department of Pathology, University Medical Center St Radboud, Nijmegen, The Netherlands
2Department of Genetics, Division of Clinical Sciences, National Cancer Institute/NIH, Bethesda, MD, USA
3Department of Molecular Cell Biology, University of Maastricht, Maastricht, The Netherlands
4Applied Spectral Imaging, Migdal Ha'Emek, Israel
5Aperio Technologies, Carlsbad, CA, USA
6Children's Hospital Research Institute, Los Angeles, CA, USA

Received 19 October 2000; Accepted 20 February 2001

Copyright © 2001 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We have investigated the use of spectral imaging for multi‐color analysis of permanent cytochemical dyes and enzyme precipitates on cytopathological specimens. Spectral imaging is based on Fourier‐transform spectroscopy and digital imaging. A pixel‐by‐pixel spectrum‐based color classification is presented of single‐, double‐, and triple‐color in situ hybridization for centromeric probes in T24 bladder cancer cells, and immunocytochemical staining of nuclear antigens Ki‐67 and TP53 in paraffin‐embedded cervical brush material (AgarCyto). The results demonstrate that spectral imaging unambiguously identifies three chromogenic dyes in a single bright‐field microscopic specimen. Serial microscopic fields from the same specimen can be analyzed using a spectral reference library. We conclude that spectral imaging of multi‐color chromogenic dyes is a reliable and robust method for pixel color recognition and classification. Our data further indicate that the use of spectral imaging (a) may increase the number of parameters studied simultaneously in pathological diagnosis, (b) may provide quantitative data (such as positive labeling indices) more accurately, and (c) may solve segmentation problems currently faced in automated screening of cell‐ and tissue specimens. Figures on‐3/macville.htm.