FT-IR microspectroscopic imaging of prostate tissue sections

Different cluster image reassembling methodologies have been used to generate infrared maps from FT-IR rriicrospectra of human prostate tissue sections. Spectra were collected in transmission mode with high spatial resolution by the use of a HgCdTe focal plane array detector imaging system. While univariate imaging techniques such as chemical mapping often give unsatisfactory classification results, unsupervised multivariate data analysis techniques such as agglomerative hierarchical clustering, fuzzy C-means, or k-means clustering confirmed standard histopathological techniques and turned out to be helpful to identify and to discriminate tissues structures. The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to chemical mapping. Among the cluster imaging methods, agglomerative hierarchical clustering (Ward's algorithm) turned out to be the best method in terms of tissue structure differentiation.