Skip to Main Content
Elasticity is an important indicator of tissue health, with increased stiffness pointing to an increased risk of cancer. We investigated a tissue inclusion characterization method for the application of early breast tumor identification. A tactile sensation imaging system (TSIS) is developed to capture images of the embedded lesions using total internal reflection principle. From tactile images, we developed a novel method to estimate that size, depth, and elasticity of the embedded lesion using 3-D finite-element-model-based forward algorithm, and neural-network-based inversion algorithm are employed. The proposed characterization method was validated by the realistic tissue phantom with inclusions to emulate the tumors. The experimental results showed that, the proposed characterization method estimated the size, depth, and Young's modulus of a tissue inclusion with 6.98%, 7.17%, and 5.07% relative errors, respectively. A pilot clinical study was also performed to characterize the lesion of human breast cancer patients using TSIS.