Elasticity Signal and Image Processing Sensor and Algorithms for Tissue Characterization

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...

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Bibliographic Details
Published in:Sensors & transducers 2014-01, Vol.163 (1), p.330-330
Main Authors: Lee, Jong-Ha, Park, Hee-Jun, Eum, Nyeon-Sik, Yoon, Hyuk-Jun, Kim, Yoon-Nyun
Format: Article
Language:English
Subjects:
Algorithms
Breast
Cameras
Clinical trials
Elasticity
Finite element analysis
Inclusions
Mathematical models
Neural networks
Parameter estimation
Pathology
Sensors
Tumors
Ultrasonic imaging
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Summary: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. [PUBLICATION ABSTRACT]
ISSN:2306-8515
1726-5479
1726-5479