Collagen morphology and texture analysis: from statistics to classification

Citations Over TimeTop 10% of 2013 papers

Abstract

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.

Related Papers

• → Leukocytes: The Double‐Edged Sword in Fibrosis(2015)49 cited
• → Identifying crossing collagen fibers in human corneal tissues using pSHG images(2019)24 cited
• → The Potential Use of Cannabis in Tissue Fibrosis(2021)21 cited
• → Texture feature extraction using gray level gradient based co-occurence matrices(2002)47 cited
• → Role of Matrix Proteins in Balancing Tissue Stiffness and Inflammation in Fibrosis(2018)