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 tactile data obtained at the surface of the tissue using an optical tactile sensation imaging system (TSIS). A forward algorithm is designed to comprehensively predict the tactile data based on the mechanical properties of tissue inclusion using finite element modeling (FEM). 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 tactile data. We utilize the artificial neural network (ANN) for the inversion algorithm. The proposed estimation method was validated by a 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%, 3.82%, and 2.51% relative errors, respectively. The obtained results prove that the proposed method has potential to become a useful screening and diagnostic method for breast cancer.
Highlights
During the past two decade, various methods have been devised for measuring or estimating the soft tissue elasticity [1]
Sonoelastography uses real-time ultrasound Doppler techniques to image the vibration pattern resulting from the propagation of low frequency shear waves that are propagated through the tissue [10,11,12]
The objective of this research is to develop a methodology to estimate the mechanical properties of an embedded tumor based on the data obtained by the tactile sensation imaging system (TSIS)
Summary
During the past two decade, various methods have been devised for measuring or estimating the soft tissue elasticity [1]. AFM has been utilized to detect tissue elasticity; this is for small local area measurements [3]. Both MRE and AFM are extremely expensive and cumbersome techniques. Sonoelastography uses real-time ultrasound Doppler techniques to image the vibration pattern resulting from the propagation of low frequency shear waves that are propagated through the tissue [10,11,12]. It is difficult to compare each technology because both technologies are at their infancy, from the physics point of view, ultrasound will suffer from limited contrast and resolution problems
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