Extraction of collagen morphological features from second-harmonic generation microscopy images via GLCM and CT analyses: A cross-laboratory study

Abstract

Second-harmonic generation (SHG) microscopy provides a high-resolution label-free approach for noninvasively detecting collagen organization and its pathological alterations. Up to date, several imaging analysis algorithms for extracting collagen morphological features from SHG images—such as fiber size and length, order and anisotropy—have been developed. However, the dependence of extracted features on experimental setting represents a significant obstacle for translating the methodology in the clinical practice. We tackled this problem by acquiring SHG images of the same kind of collagenous sample in various laboratories using different experimental setups and imaging conditions. The acquired images were analyzed by commonly used algorithms, such as gray-level co-occurrence matrix or curvelet transform; the extracted morphological features were compared, finding that they strongly depend on some experimental parameters, whereas they are almost independent from others. We conclude with useful suggestions for comparing results obtained in different labs using different experimental setups and conditions.