Analysis of Spectroscopic Imaging Data by Fuzzy C-Means Clustering

A novel method of analyzing spectroscopic imaging data is presented. A fuzzy C-means clustering algorithm has been applied to the analysis of near-infrared spectroscopic imaging data acquired with the combination of a CCD camera and a liquid crystal tunable filter. The use of fuzzy C-means clustering dramatically increased the information obtained from near-IR spectroscopic images and allowed for the detection of small subregions of the image that contained novel and unanticipated spectral features, without the need for a priori knowledge of the chemical composition of the sample. Two illustrative samples were analyzed, one comprised of four different inks printed on label paper and the other containing indocyanine green and human blood patches. The regions containing the different constituents were clearly demarcated and their mean spectra determined. The mean spectra of the second sample were shown to match those obtained using a scanning near-IR spectrometer. In addition to probing the spatial and spectral characteristics of the samples, the fuzzy C-means clustering analysis also helped improve the signal-to-noise ratio of the spectra.