Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods

Yuming Liu, Adib Keikhosravi, Carolyn A. Pehlke, Jeremy S. Bredfeldt, Matthew Dutson, Haixiang Liu, Guneet S. Mehta, Robert Claus, Akhil J. Patel, Matthew W. Conklin, David R. Inman, Paolo P. Provenzano, Eftychios Sifakis, Jignesh M. Patel, Kevin W. Eliceiri

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of in vivo mouse xenograft breast cancer images.

Original languageEnglish (US)
Article number198
JournalFrontiers in Bioengineering and Biotechnology
StatePublished - Apr 21 2020

Bibliographical note

Publisher Copyright:
© Copyright © 2020 Liu, Keikhosravi, Pehlke, Bredfeldt, Dutson, Liu, Mehta, Claus, Patel, Conklin, Inman, Provenzano, Sifakis, Patel and Eliceiri.


  • breast cancer
  • collagen organization
  • curvelet transform
  • fibrillar collagen
  • image analysis software
  • pancreatic cancer
  • second harmonic generation microscopy
  • tumor microenvironment


Dive into the research topics of 'Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods'. Together they form a unique fingerprint.

Cite this