Elasticity Signal and Image Processing Sensor and Algorithms for Tissue Characterization

Abstract

The tissue inclusion parameter estimation method is proposed to measure the stiffness as well as geometric parameters. The estimation is performed based on the elasticity image obtained at the surface of the tissue using an optical based elasticity imaging sensor. A forward algorithm is designed to comprehensively predict the elasticity image based on the mechanical properties of tissue inclusion using finite element modeling. This forward information is used to develop an inversion algorithm that will be used to extract the size, depth, and Young's modulus of a tissue inclusion from the elasticity image. We utilize the artificial neural network (ANN) for inversion algorithm. The proposed estimation method was validated by the realistic tissue phantom with stiff inclusions. The experimental results showed that the proposed estimation method can measure the size, depth, and Young's modulus of a tissue inclusion with 0.58%, 1.12%, and 0.51% relative errors, respectively. A small-scale of breast cancer patient experiments is also presented. The obtained results prove that the proposed method has potential to become a screening and diagnostic method for breast tumor.