Abstract
Collagen fiber alignment derived from second harmonic generation (SHG) microscopy images can be important for disease diagnostics. Image processing algorithms are needed to robustly quantify the alignment in images with high sensitivity and reliability. Fourier transform (FT) magnitude, 2D power spectrum, and image autocorrelation have previously been used to extract fiber information from images by assuming a certain mathematical model (e.g. Gaussian distribution of the fiber-related parameters) and fitting. The fitting process is slow and fails to converge when the data is not Gaussian. Herein we present an efficient constant-time deterministic algorithm which characterizes the symmetricity of the FT magnitude image in terms of a single parameter, named the fiber alignment anisotropy R ranging from 0 (randomized fibers) to 1 (perfect alignment). This represents an important improvement of the technology and may bring us one step closer to utilizing the technology for various applications in real time. In addition, we present a digital image phantom-based framework for characterizing and validating the algorithm, as well as assessing the robustness of the algorithm against different perturbations.
Highlights
second harmonic generation (SHG) microscopy is a powerful technology which allows for direct, non-invasive, and labelfree three-dimensional visualization of collagen fiber architecture and extracellular matrix (ECM) of biological tissues
Many studies have shown that SHG microscopy can be used for disease diagnosis, and links have been established between ECM remodeling and disease formation and progression
The risk of breast cancer initiation is associated with high collagen density [1], and it has been shown that cancer cells interact with ECM to promote metastasis [2, 3]
Summary
SHG microscopy is a powerful technology which allows for direct, non-invasive, and labelfree three-dimensional visualization of collagen fiber architecture and extracellular matrix (ECM) of biological tissues. Multiple research groups have presented the use of Fourier Transform (FT) to characterize the morphological properties of collagen fibers [14,15,16] This has been performed in several ways, such as by thresholding and fitting the 2D image autocorrelation to a Gaussian function [16] or an ellipse [17], or alternatively by fitting the image power spectrum (squared magnitude of the FT output) to a line [14], or by fitting the angular distribution to a Gaussian function [8]. Fitting in general is a slow process, especially when there are multiple unknowns and perhaps even more time-consuming if the data is not complying with the target form To overcome these limitations, we have developed a deterministic, unbiased approach, which does not require fitting or thresholding. The method is fast and robust, which is critical for real-time fiber alignment processing in in vivo imaging applications such as SHG endomicroscopy of biological tissues [9, 18]
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