Digital Image Correlation for Improved Detection of Basal Cell Carcinoma

Border detection is a critical aspect during removal of a basal cell carcinoma tumor. Since the tumor is only 3% to 50% as stiff as the healthy skin surrounding it, strain concentrates in the tumor during deformation. Here we develop a digital image correlation (DIC) technique for improved lateral border detection based upon the strain concentrations associated with the stiffness difference of healthy and cancerous skin. Gelatin skin phantoms and pigskin specimens are prepared with compliant inclusions of varying shapes, sizes, and stiffnesses. The specimens with inclusions as well as several control specimens are loaded under tension, and the full-field strain and displacement fields measured by DIC. Significant strain concentrations develop around the compliant inclusions in gelatin skin phantoms, enabling detection of the tumor border to within 2% of the actual border. At a lower magnification, the lateral border between a pigskin/inclusion interface is determined within 23% of the border. Strain concentrations are identified by DIC measurements and associated with the lateral edges of the compliant inclusions. The experimental DIC protocol developed for model specimens has potential as a tool to aid in more accurate detection of basal cell carcinoma borders.