Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods - Liu, Yuming; Keikhosravi, Adib; Pehlke, Carolyn A.; Bredfeldt, Jeremy S.; Dutson, Matthew; Liu, Haixiang; Mehta, Guneet S.; Claus, Robert; Patel, Akhil J.; Conklin, Matthew W.; Inman, David R.; Provenzano, Paolo P.; et al. (Frontiers in Bioengineering and Biotechnology, 2020)
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 curvelets 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.