Graphical Methods for Quantifying Macromolecules through Bright Field Imaging
Bright field imaging of biological samples stained with antibodies and/or special stains provides
a rapid protocol for visualizing various macromolecules. However, this method of sample staining and imaging is rarely
employed for direct quantitative analysis due to variations
in sample fixations, ambiguities introduced by color composition,
and the limited dynamic range of imaging instruments.
We demonstrate that, through
the decomposition of color signals, staining can be scored on a cell-by-cell
basis. We have applied our method to fibroblasts grown from histologically
normal breast tissue biopsies obtained from
two distinct populations. Initially, nuclear regions are segmented
through conversion of color images into gray scale, and
detection of dark elliptic features.
Subsequently, the strength of staining is quantified
by a color decomposition model that is optimized
by a graph cut algorithm.
In rare cases where nuclear signal is significantly altered
as a result of sample preparation, nuclear segmentation can
be validated and corrected.
Finally, segmented stained patterns are associated with each
nuclear region following region-based tessellation.
Compared to classical non-negative matrix factorization,
proposed method (i) improves color decomposition, (ii) has a better
noise immunity, (iii) is more invariant to initial conditions, and (iv)
has a superior computing performance.
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