Shear wave imaging and classification using extended Kalman filter and decision tree algorithm

Shear wave ultrasound elastography is a quantitative imaging approach in soft tissues based on viscosity-elastic properties. Complex shear modulus (CSM) estimation is an effective solution to analyze tissues' physical properties for elasticity and viscosity based on the wavenumber and attenuati...

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Autores principales:	Tran Quang-Huy, Phuc Thinh Doan, Nguyen Thi Hoang Yen, Duc-Tan Tran
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	shear wave elastography elasticity viscosity complex shear modulus extended kalman filter decision tree Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/e28565d8726244ff93c893d141cae6d7
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id	oai:doaj.org-article:e28565d8726244ff93c893d141cae6d7
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spelling	oai:doaj.org-article:e28565d8726244ff93c893d141cae6d72021-11-23T02:26:53ZShear wave imaging and classification using extended Kalman filter and decision tree algorithm10.3934/mbe.20213781551-0018https://doaj.org/article/e28565d8726244ff93c893d141cae6d72021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021378?viewType=HTMLhttps://doaj.org/toc/1551-0018Shear wave ultrasound elastography is a quantitative imaging approach in soft tissues based on viscosity-elastic properties. Complex shear modulus (CSM) estimation is an effective solution to analyze tissues' physical properties for elasticity and viscosity based on the wavenumber and attenuation coefficient. CSM offers a way to detect and classify some types of soft tissues. However, CSM-based elastography inherits some obstacles, such as estimation precision and calculation complexity. This work proposes an approach for two-dimensional CSM estimation and soft tissue classification using the Extended Kalman Filter (EKF) and Decision Tree (DT) algorithm, named the EKF-DT approach. CSM estimation is obtained by applying EKF to exploit shear wave propagation at each spatial point. Afterward, the classification of tissues is done by a direct and efficient decision tree algorithm categorizing three types of normal, cirrhosis, and fibrosis liver tissues. Numerical simulation scenarios have been employed to illustrate the recovered quality and practicality of the proposed method's liver tissue classification. With the EKF, the estimated wave number and attenuation coefficient are close to the ideal values, especially the estimated wave number. The states of three liver tissue types were automatically classified by applying the DT coupled with two proposed thresholds of elasticity and viscosity: (2.310 kPa, 1.885 Pa.s) and (3.620 kPa 3.146 Pa.s), respectively. The proposed method shows the feasibility of CSM estimation based on the wavenumber and attenuation coefficient by applying the EKF. Moreover, the DT can automate the classification of liver tissue conditions by proposing two thresholds. The proposed EKF-DT method can be developed by 3D image reconstruction and empirical data before applying it in medical practice.Tran Quang-Huy Phuc Thinh DoanNguyen Thi Hoang YenDuc-Tan TranAIMS Pressarticleshear wave elastography elasticityviscositycomplex shear modulusextended kalman filterdecision treeBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7631-7647 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	shear wave elastography elasticity viscosity complex shear modulus extended kalman filter decision tree Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	shear wave elastography elasticity viscosity complex shear modulus extended kalman filter decision tree Biotechnology TP248.13-248.65 Mathematics QA1-939 Tran Quang-Huy Phuc Thinh Doan Nguyen Thi Hoang Yen Duc-Tan Tran Shear wave imaging and classification using extended Kalman filter and decision tree algorithm
description	Shear wave ultrasound elastography is a quantitative imaging approach in soft tissues based on viscosity-elastic properties. Complex shear modulus (CSM) estimation is an effective solution to analyze tissues' physical properties for elasticity and viscosity based on the wavenumber and attenuation coefficient. CSM offers a way to detect and classify some types of soft tissues. However, CSM-based elastography inherits some obstacles, such as estimation precision and calculation complexity. This work proposes an approach for two-dimensional CSM estimation and soft tissue classification using the Extended Kalman Filter (EKF) and Decision Tree (DT) algorithm, named the EKF-DT approach. CSM estimation is obtained by applying EKF to exploit shear wave propagation at each spatial point. Afterward, the classification of tissues is done by a direct and efficient decision tree algorithm categorizing three types of normal, cirrhosis, and fibrosis liver tissues. Numerical simulation scenarios have been employed to illustrate the recovered quality and practicality of the proposed method's liver tissue classification. With the EKF, the estimated wave number and attenuation coefficient are close to the ideal values, especially the estimated wave number. The states of three liver tissue types were automatically classified by applying the DT coupled with two proposed thresholds of elasticity and viscosity: (2.310 kPa, 1.885 Pa.s) and (3.620 kPa 3.146 Pa.s), respectively. The proposed method shows the feasibility of CSM estimation based on the wavenumber and attenuation coefficient by applying the EKF. Moreover, the DT can automate the classification of liver tissue conditions by proposing two thresholds. The proposed EKF-DT method can be developed by 3D image reconstruction and empirical data before applying it in medical practice.
format	article
author	Tran Quang-Huy Phuc Thinh Doan Nguyen Thi Hoang Yen Duc-Tan Tran
author_facet	Tran Quang-Huy Phuc Thinh Doan Nguyen Thi Hoang Yen Duc-Tan Tran
author_sort	Tran Quang-Huy
title	Shear wave imaging and classification using extended Kalman filter and decision tree algorithm
title_short	Shear wave imaging and classification using extended Kalman filter and decision tree algorithm
title_full	Shear wave imaging and classification using extended Kalman filter and decision tree algorithm
title_fullStr	Shear wave imaging and classification using extended Kalman filter and decision tree algorithm
title_full_unstemmed	Shear wave imaging and classification using extended Kalman filter and decision tree algorithm
title_sort	shear wave imaging and classification using extended kalman filter and decision tree algorithm
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/e28565d8726244ff93c893d141cae6d7
work_keys_str_mv	AT tranquanghuy shearwaveimagingandclassificationusingextendedkalmanfilteranddecisiontreealgorithm AT phucthinhdoan shearwaveimagingandclassificationusingextendedkalmanfilteranddecisiontreealgorithm AT nguyenthihoangyen shearwaveimagingandclassificationusingextendedkalmanfilteranddecisiontreealgorithm AT ductantran shearwaveimagingandclassificationusingextendedkalmanfilteranddecisiontreealgorithm
_version_	1718417408823132160

Shear wave imaging and classification using extended Kalman filter and decision tree algorithm

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