Detection and classification of soft tissues using complex shear modulus estimation and decision tree algorithm

Complex shear modulus (CSM) estimation is an effective facility to analyze the mechanical properties of tissues in terms of elasticity and viscosity. CSM can be used to detect and classify some kinds of soft tissues. However, the challenge is the estimation accuracy, and computational complexity. In...

Full description

Saved in:
Bibliographic Details
Main Authors: Luong Quang Hai, Nguyen Manh Cuong, Tran Duc-Tan
Format: Conference Proceeding
Language:English
Subjects:
Attenuation
Biological tissues
Classification algorithms
complex shear modulus
decision tree
Elasticity
Estimation
extended Kalman filter
Liver
Shear wave elasticity imaging
Viscosity
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Complex shear modulus (CSM) estimation is an effective facility to analyze the mechanical properties of tissues in terms of elasticity and viscosity. CSM can be used to detect and classify some kinds of soft tissues. However, the challenge is the estimation accuracy, and computational complexity. In this paper, we propose a 2D CSM estimation and classification of soft tissues based on the Extended Kalman Filter (EKF) and the Decision Tree (DT) algorithm. EKF is used to estimate the CSM at each spatial point by exploiting the shear wave propagation. A simple and effective decision tree algorithm is then developed for the classification of three kinds of tissues. Simulated experiment and performance study are carried out to confirm the quality of the proposed method.
ISSN:2162-1039
DOI:10.1109/ATC.2017.8167619