Widely used for medical analysis, the texture of the human
scar tissue is characterized by irregular and extensive types. The quantitative detection and analysis of the
scar texture as enabled by image analysis technology is of great significance to clinical practice. However, the existing methods remain disadvantaged by various shortcomings, such as the inability to fully extract the features of texture. Hence, the integration of second harmonic generation (SHG) imaging and deep learning algorithm is proposed in this study. Through combination with Tamura texture features, a regression model of the
scar texture can be constructed to develop a novel method of computer-aided diagnosis, which can assist clinical diagnosis. Based on wavelet packet transform (WPT) and generative adversarial network (GAN), the model is trained with
scar texture images of different ages. Generalized Boosted Regression Trees (GBRT) is also adopted to perform regression analysis. Then, the extracted features are further used to predict the age of
scar. The experimental results obtained by our proposed model are better compared to the previously published methods. It thus contributes to the better understanding of the mechanism behind
scar development and possibly the further development of SHG for skin analysis and clinic practice.