Polarization Optical Detection and Localization of Subcutaneous Lesions

It is of great significance in clinical diagnosis and treatment evaluation to accurately identify the lesion tissue and further extract its characteristics and depth location. In this letter, we construct tissue phantoms for three lesion types: fibrosis (FT), organelle proliferation (OPT), and pigmentation (PT). These phantoms allow for the quantitative regulation of mimicked disease depth. The experimental results show that the parameter Kc, combined with MMT parameters, can effectively distinguish the presence of lesions and their abnormal types. Further, the study extracts depth-resolved polarization feature parameters (DSPFPs) for different lesion types. By experiments of tissue phantoms with various depth setting, the established machine learning regression algorithms based on DSPFPs demonstrate their depth retrieval capabilities.