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J Biomed Opt. 2014 Jan;19(1):16007. doi: 10.1117/1.JBO.19.1.016007.

Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer.

Author information

1
University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706bMorgridge Institute for Research, 330 North Orchard Street, Madison, Wisconsin 53715.
2
University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706.
3
University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706cUniversity of Wisconsin at Madison, Laboratory for Cell and Molecular Biology, 1525 Linden Drive, Madison, Wisco.
4
University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706dUniversity of Wisconsin at Madison, Department of Electrical and Computer Engineering, 1415 Engineering Drive, M.
5
University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706bMorgridge Institute for Research, 330 North Orchard Street, Madison, Wisconsin 53715cUniversity of Wisconsin at.

Abstract

Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.

PMID:
24407500
PMCID:
PMC3886580
DOI:
10.1117/1.JBO.19.1.016007
[Indexed for MEDLINE]
Free PMC Article

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