A differential wavelet-based noise reduction approach to improve clustering of hyperspectral Raman imaging data

Raman spectral imaging has been widely used for extracting chemical information from biological specimens. One of the challenging problems is to cluster the chemical groups from the vast amount of hyperdimensional spectral imaging data so that functionally similar groups can be identified. Moreover, the poor signal to noise ratio makes the problem more difficult. In the paper, we introduce a novel approach that combines a differential wavelet based noise reduction approach with a fuzzy clustering algorithm for the classification of chemical groups. The discrimination of true spectral features and noises was facilitated by decomposing the spectral data in the differential wavelet transform domain. The performance of the proposed approach was evaluated by the improvement over the subsequent clustering of a dentin/adhesive interface specimen under different noise levels. In comparison with conventional smoothing algorithms, the proposed approach demonstrates better performance