Collagen morphology and texture analysis: from statistics to classification

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Mostaço-Guidolin, Leila B.
Ko, Alex C.-T.
Wang, Fei
Xiang, Bo
Hewko, Mark
Tian, Ganghong
Major, Arkady
Shiomi, Masashi
Sowa, Michael G.
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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 such as gray level co-occurrence matrix (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 re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage.
Computational biophysics, Biophotonics, Imaging techniques, Atherosclerosis
Mostaço-Guidolin, L.B. et al. Collagen morphology and texture analysis: from statistics to classification. Scientific Reports, 3: 2190.