Bio-optics based sensation imaging for breast tumor detection using tissue characterization

Sensors
Jong-Ha LeeHee-Jun Park

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.

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Citations

Jul 29, 2018·Journal of Biophotonics·Wes M AllenBrendan F Kennedy
May 3, 2019·Technology and Health Care : Official Journal of the European Society for Engineering and Medicine·Hojong ChoiJae-Myung Ryu
Mar 13, 2019·World Journal of Radiology·Ge-Ge WuChristoph F Dietrich

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Methods Mentioned

BETA
atomic force microscopy
AFM

Software Mentioned

FEM
LOOCV
ANSYS

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