Distinguishing Malignant Melanoma and Benign Nevus of Human Skin by Retardance Using Mueller Matrix Imaging Polarimeter

Malignant melanoma is considered the most serious type of skin cancer. In clinical practice, the conventional technique based on subjective visual examination has a high rate of misdiagnosis for malignant melanoma and benign nevus. Polarization imaging techniques have great potential in clinical diagnosis due to the advantages of improving sensitivity to functional structures, such as microfiber. In this paper, a set of human skin tissue sections, including 853 normal, 851 benign nevus, and 874 malignant melanoma, were analyzed and differentiated using a homemade high-fidelity Mueller matrix imaging polarimeter. The quantitative result using support vector machine algorithms confirmed that, while scalar retardance yields lower accuracy rates, vectorial retardance results in greater accuracy for both the training and testing sets. In particular, the cross-validation accuracy for the training set increased from 88.33% to 98.60%, and the prediction accuracy for the testing set increased from 87.92% to 96.19%. This tackles the limitation of the examination based on clinical experience and suggests that vectorial retardance can provide more accurate diagnostic evidence than scalar retardance. Unfortunately, it is inconvenient and time-consuming to read and analyze each component of the vectorial retardance simultaneously in the qualitative assessment. To address this clinical challenge, a color-encoded vectorial retardance imaging method was implemented. This method can provide superior tissue-specific contrast and more fiber details than scalar retardance. The anisotropic microfiber variation among different skin lesions, including the orientation and distribution, can be clearly highlighted. We believe that this work will not only enable early and rapid diagnosis of skin cancer but also provide a good observation and analysis of the state of cancer progression.