This work developed a phase congruency algorithm combined with texture analysis to quantitatively characterize collagen morphology in second-harmonic generation (SHG) images from human scars. The extracted phase and texture parameters of the SHG images quantified collagen directionality, homogeneity, and coarseness in scars and varied with scar duration. Phase parameters showed an increasing tendency of the mean of phase congruency with scar duration, indicating that collagen fibers are better oriented over time. Texture parameters calculated from local difference local binary pattern (LD-LBP) and Haar wavelet transform, demonstrated that the LD-LBP variance decreased and the energy of all subimages increased with scar duration. It implied that collagen has a more regular pattern and becomes coarser with scar duration. In addition, the random forest regression was used to predict scar duration, demonstrating reliable performance of the extracted phase and texture parameters in characterizing collagen morphology in scar SHG images. Results indicate that the extracted parameters using the proposed method can be used as quantitative indicators to monitor scar progression with time and can help understand the mechanism of scar progression.