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.
journal_name
J Biomed Optjournal_title
Journal of biomedical opticsauthors
Bredfeldt JS,Liu Y,Pehlke CA,Conklin MW,Szulczewski JM,Inman DR,Keely PJ,Nowak RD,Mackie TR,Eliceiri KWdoi
10.1117/1.JBO.19.1.016007subject
Has Abstractpub_date
2014-01-01 00:00:00pages
16007issue
1eissn
1083-3668issn
1560-2281pii
1813425journal_volume
19pub_type
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