Collagen morphology and texture analysis: from statistics to classification.

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.

在这项研究中，我们提出了一种图像分析方法，能够量化由病理状况引起的组织胶原纤维结构的形态变化。我们探索了基于一阶统计量（FOS）和二阶统计量（如灰度共生矩阵（GLCM））的纹理分析，以提取与组织胶原网络的结构和生化变化相关的二次谐波产生（SHG）图像特征。基于这些提取的定量参数，对SHG图像进行了多组分类。结合FOS和GLCM纹理值，我们对从动脉粥样硬化动脉获取的SHG胶原图像实现了可靠分类，准确率、敏感度和特异度均>90%。所提出的方法可应用于涉及胶原重塑的多种情况，如皮肤疾病、不同类型的纤维化以及影响韧带和软骨的肌肉骨骼疾病。