NDLI: Inclusion mechanical property estimation using tactile images, finite element method, and artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Lee, Jong-Ha Won, Chang-Hee
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19122 USA (Lee, Jong-Ha; Won, Chang-Hee)
Abstract	In this paper, we developed a methodology for estimating three parameters of tissue inclusion: size, depth, and Young's modulus from the tactile data obtained at the tissue surface with the tactile sensation imaging system. The estimation method consists of the forward algorithm using finite element method, and inversion algorithm using artificial neural network. The forward algorithm is designed to comprehensively predict the tactile data based on the mechanical properties of the tissue inclusion. 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 image. The proposed method is then validated with custom made tissue phantoms with matching elasticities of typical human breast tissues. The experimental results showed that the proposed estimation method estimates the size, depth, and Young's modulus of tissue inclusions with root mean squared errors of 1.25 mm, 2.09 mm, and 28.65 kPa, respectively.
Starting Page	14
Ending Page	17
File Size	418097
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6089885
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Finite element methods Optical waveguides Estimation Phantoms Algorithm design and analysis Sensors
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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