Binary Malignancy Classification of Skin Tissue Using Reflectance and Texture Features from Macropathology Multi-Spectral Images

Abstract

This study suggests an analysis procedure on macropathology multi-spectral images(macroMSI), for visual representation of grossly malignant regions of skin samples during excision margin pathological diagnosis. We implemented binary malignancy classification on a database of ten highresolution 7-channel macroMSI tissue samples, captured before and after formalin fixing. We reconstructed spectral reflectance by Wiener estimation and described texture using local binary patterns (LBP). Highlighted malignancy regions were derived from an optimal classifier selected by cross-validated performance.The results show that malignant regions are highlighted fairly accurately and indicate the importance of analyzing unfixed tissue in conjunction with fixed tissue.