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

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.