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Spectral Imaging of Multi-Color Chromogenic Dyes in Pathological Specimens

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 pre...

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Detalles Bibliográficos
Autores principales: Macville, Merryn V. E., Van der Laak, Jeroen A. W. M., Speel, Ernst J. M., Katzir, Nir, Garini, Yuval, Soenksen, Dirk, McNamara, George, de Wilde, Peter C. M., Hanselaar, Antonius G. J. M., Hopman, Anton H.N., Ried, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2001
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617509/
https://www.ncbi.nlm.nih.gov/pubmed/11455032
http://dx.doi.org/10.1155/2001/740909
Descripción
Sumario: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 http://www.esacp.org/acp/2001/22‐3/macville.htm.