Collagen morphology and texture analysis: from statistics to classification

Thumbnail Image
Date
2013-07-12
Authors
Mostaço-Guidolin, Leila B.
Ko, Alex C.-T.
Wang, Fei
Xiang, Bo
Hewko, Mark
Tian, Ganghong
Major, Arkady
Shiomi, Masashi
Sowa, Michael G.
Journal Title
Journal ISSN
Volume Title
Publisher
Nature
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.
Description
Keywords
Computational biophysics, Biophotonics, Imaging techniques, Atherosclerosis
Citation
Mostaço-Guidolin, L.B. et al. Collagen morphology and texture analysis: from statistics to classification. Scientific Reports, 3: 2190.