Digital image correlation for improved detection of basal cell carcinoma

Border detection is an essential aspect of the surgical removal of basal cell carcinoma (BCC) tumors. We apply a digital image correlation (DIC) technique for improved border detection based upon the strain concentrations created by BCC tumors in healthy skin. Basal cell carcinoma tumors are approximately 3 to 50% as stiff as the healthy skin that surrounds them; thus comparison between the strain fields in healthy and cancerous skin could aid in identification of the tumor border. To mimic a BCC tumor embedded in skin, we used gelatin as a skin phantom by varying Bloom number and concentration of the gelatin to create a stiff matrix with a compliant inclusion. These phantoms were then tested under uniaxial tension, and DIC was used to determine the strain and displacement fields around the compliant inclusions. From analysis of the strain fields, we identified the location and shape of model basal cell tumors in a gelatin matrix. This research lays the basis for further work using DIC to detect strain differences in actual skin.