Collagen morphology assessed by textural image analysis


Research Area: Cell Biology, Tissue Microstructure, Biophotonics and Image Analysis

Authors: Leila B. Mostaço-Guidolin, Alex C.-T. Ko, Fei Wang, Bo Xiang, Mark Hewko, Ganghong Tian, Arkady Major, Masashi Shiomi & Michael G. Sowa

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics parameters such as gray-level co-occurrence matrices (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity, and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen remodeling, such as in skin disorders, different types of fibrosis, and muscular-skeletal diseases affecting ligaments and cartilage.

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