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Wood BR, Bambery KR, Evans CJ, et al. A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections. BMC Med Imaging. 2006;6:12doi: 10.1186/1471-2342-6-12.
Wood, B. R., Bambery, K. R., Evans, C. J., Quinn, M. A., & McNaughton, D. (2006). A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections. BMC medical imaging, 612. https://doi.org/10.1186/1471-2342-6-12
Wood, Bayden R, et al. "A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections." BMC medical imaging vol. 6 (2006): 12. doi: https://doi.org/10.1186/1471-2342-6-12
Wood BR, Bambery KR, Evans CJ, Quinn MA, McNaughton D. A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections. BMC Med Imaging. 2006 Oct 03;6:12. doi: 10.1186/1471-2342-6-12. PMID: 17014733; PMCID: PMC1592472.
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BMC Med Imaging. 2006 Oct 03;6:12. doi: 10.1186/1471-2342-6-12.

A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections.

BMC medical imaging

Bayden R Wood, Keith R Bambery, Corey J Evans, Michael A Quinn, Don McNaughton

Affiliations

  1. Centre for Biospectroscopy and School of Chemistry, Monash University, 3800 Victoria, Australia. [email protected]

PMID: 17014733 PMCID: PMC1592472 DOI: 10.1186/1471-2342-6-12

Abstract

BACKGROUND: Three-dimensional (3D) multivariate Fourier Transform Infrared (FTIR) image maps of tissue sections are presented. A villoglandular adenocarcinoma from a cervical biopsy with a number of interesting anatomical features was used as a model system to demonstrate the efficacy of the technique.

METHODS: Four FTIR images recorded using a focal plane array detector of adjacent tissue sections were stitched together using a MATLAB routine and placed in a single data matrix for multivariate analysis using Cytospec. Unsupervised Hierarchical Cluster Analysis (UHCA) was performed simultaneously on all 4 sections and 4 clusters plotted. The four UHCA maps were then stacked together and interpolated with a box function using SCIRun software.

RESULTS: The resultant 3D-images can be rotated in three-dimensions, sliced and made semi-transparent to view the internal structure of the tissue block. A number of anatomical and histopathological features including connective tissue, red blood cells, inflammatory exudate and glandular cells could be identified in the cluster maps and correlated with Hematoxylin & Eosin stained sections. The mean extracted spectra from individual clusters provide macromolecular information on tissue components.

CONCLUSION: 3D-multivariate imaging provides a new avenue to study the shape and penetration of important anatomical and histopathological features based on the underlying macromolecular chemistry and therefore has clear potential in biology and medicine.

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