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Bioinformatics Advance Access published online on August 14, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn426
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Graphical Methods for Quantifying Macromolecules through Bright Field Imaging

Hang Chang 1,2, Rosa Anna DeFilippis 3, Thea D. Tlsty 3 and Bahram Parvin 1,4

1Lawrence Berkeley National Laboratory, Berkeley, CA 94720.
2Institute of Automation, Chinese Academy of Sciences, Beijing.
3Department of Pathology, University of California, San Francisco
4Department of Electrical Engineering, University of California, Riverside

To whom correspondence should be addressed. Hang Chang, E-mail: hchang{at}lbl.gov


  Abstract

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 is scored on a cell-by-cell basis. We applied our method to fibroblasts grown from histologically normal breast tissue biopsies obtained from two distinct populations. Firstly, nuclear regions are initially 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, stained patterns are associated with each nuclear region following region-based tessellation. Compared to classical non-negative matrix factorization, our new 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.

Associate Editor: Dr. Jonathan Wren

Received on March 12, 2008; revised on August 9, 2008; accepted on August 10, 2008

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