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 parameters such as gray-level co-occurrence matrices (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 remodeling, such as in skin disorders, different types of fibrosis, and muscular-skeletal diseases affecting ligaments and cartilage.