Robust Collagen Texture Quantification in Nonlinear Microscopy by Combining the Gradient Structure Tensor With a...

Area of Science:

• Biomedical Imaging
• Materials Science
• Computational Biology

Background:

• Collagen structure derangement is a key indicator in various diseases.
• Quantitative analysis of collagen organization from microscopy images is crucial for early disease detection.
• Image noise can significantly impact the accuracy of collagen quantification.

Purpose of the Study:

• To introduce and validate a novel methodology for robust quantification of collagen fiber direction, dispersion, and degree of anisotropy (DA).
• To assess the method's accuracy and robustness against varying levels of image noise.
• To demonstrate the method's potential in analyzing collagen remodeling in pathological conditions.

Main Methods:

• Developed a novel methodology reinforcing gradient structure tensor computation with a mixed noise model.
• Validated the method on a synthetic image dataset generated by a vector field-based fiber generator.
• Assessed robustness against increasing image noise levels and accuracy in distinguishing fiber organization.

Main Results:

• Accurate estimation of fiber angle direction with errors < 2 degrees for signal-to-noise ratios (SNR) down to 5.
• Local and global degree of anisotropy (DA) effectively distinguished fiber organization levels even at high noise (SNR=5).
• Small accuracy errors observed for local (<0.04) and global (<0.06) DA on realistic patterns.

Conclusions:

• The novel method provides robust quantification of collagen structure, improving accuracy in the presence of image noise.
• The technique shows potential for investigating collagen structural remodeling in fibrosis-related diseases.
• Further tuning for specific tissues and clinical problems is recommended for future applications.