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Raman Imaging for Biomedical Applications in Clinics 293

make use of the full spectral information, and that are less sensitive to
noise, can also help lower the needed spectral quality, and thus acqui-
sition time, to extract the clinical information from the spectra.
Although simple techniques like calculating band ratios can offer
informative and easily interpretable images, the spectral quality that
is needed to get a detailed image is much higher than for multivariate
techniques that use the whole spectrum. Of the multivariate tech-
niques, cluster analysis has become popular because of its easy of use
and power to yield meaningful images without the need for any sub-
jective input. However, techniques strain the large variations that are
present in the data, which need not be the interesting ones. More
sophisticated techniques exist that also use the spatial distribution of
the spectra to obtain image segmentations, but these have not yet
been extensively used in biomedical imaging. 83
In conclusion, we think that Raman imaging can develop from a
research tool into a widely used clinical diagnostic tool as many appli-
cations have shown (part of) its diagnostic potential. However, more
technical progress is needed to reduce the acquisition time of tissue
Raman images and better extract all the clinically relevant information
from the multidimensional images. Considering the long way from the
discovery of the Raman effect to its application in biomedical research,
and considering the increasing interest in the technique, we expect that
this can be realized in a relatively short period of time.

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