Biomedical Engineering Reference
In-Depth Information
a number of different spectra based on their spectral properties. This approach
involves only a minimum of a priori information such as the number of stains that
are expected to be in the image.
One of the most common non-supervised algorithms is principle component
analysis (PCA). It uses statistical analysis of all the spectra in the image and finds
their similarities and differences [ 67 ]. Using that, the statistical analysis finds a small
set of spectra that spans the whole data set. The first spectrum in the set is the one
that contributes the most to the spectra in the image; the second one contributes less,
and so on. The principle spectra that are found by PCA can represent special classes
of the measured objects such as cancer and normal cells.
4.6
Applications
Spectral imaging is important whenever the spectrum contains useful information
on the spatial distribution of entities in an image. The spectral information enables
to distinguish between different mixtures of materials or entities even if they have
a similar color (or overlapping spectra). This permits one to label different entities
in a sample simultaneously and to quantitatively analyze each one of them. The
spectral information is also useful in differing between the spectrum of a given
stain and the spectrum of artifacts that might contribute to the measured image as
happens seldom due to auto-fluorescence that are physically significant in some
cases but biologically irrelevant. It is also possible to study spectral changes which
are indicative of a process that the sample undergoes. We will briefly describe few
different possible applications.
4.6.1
Observation of Combinatorial-Labeled Entities
In many cases, there is a need to observe and analyze many entities simultaneously.
They may either have natural different colors, or they may be labeled with different
dyes. These entities can be a number of proteins, genes, or other biological
organelles. It may be important to observe the number of each species and their
spatial distribution and co-localization. Even though there are hundreds of different
dyes, many of them have a similar color so that only a few can be distinguished by
eye or by a simple color filtering technique. Spectral imaging enables to overcome
this obstacle.
When working with fluorescence, it is necessary to excite all the dyes, and the
emission spectral range cannot overlap with the excitation range. Therefore, part of
the spectral range that is used for excitation cannot be used for detection, multiband
filters are used, and none of the fluorochromes' excitation band can overlap with the
emission of any other.
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