Local Motion Intensity Clustering (LMIC) Model for Segmentation of Right Ventricle in Cardiac MRI Images

TY - JOUR

T1 - Local Motion Intensity Clustering (LMIC) Model for Segmentation of Right Ventricle in Cardiac MRI Images

AU - Guo, Zengzhi

AU - Tan, Wenjun

AU - Wang, Lu

AU - Xu, Lisheng

AU - Wang, Xinhui

AU - Yang, Benqiang

AU - Yao, Yudong

N1 - Publisher Copyright: © 2018 IEEE.

PY - 2019/3

Y1 - 2019/3

N2 - Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods.

AB - Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods.

KW - Segmentation

KW - local motion intensity clustering (LMIC) model

KW - magnetic resonance imaging

KW - right ventricle

UR - https://www.scopus.com/pages/publications/85044728073

UR - https://www.scopus.com/pages/publications/85044728073#tab=citedBy

U2 - 10.1109/JBHI.2018.2821709

DO - 10.1109/JBHI.2018.2821709

M3 - Article

C2 - 29994105

SN - 2168-2194

VL - 23

SP - 723

EP - 730

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

IS - 2

M1 - 8329241

ER -