Raman imaging is a hyperspectral approach able to provide information on the spatial distribution of a particular biochemical feature without the use of any staining or sample processing. The extraction of the relevant information from the large dataset obtained however is a laborious and complex task that still requires the development of robust chemometric approaches. In this paper, we propose a general framework for analyzing data acquired by a commercial Raman spectrometers. This framework is based both on exploiting spectral information and unsupervised clustering, in order to clearly identify the borders and the compositions of different regions of interest. Finally, we describe an efficient GPU-based parallelization, which ensures a fast image classification.
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