标题:High-Order Markov Random Field Based Image Registration for Pulmonary CT
作者:Xue, Peng ;Dong, Enqing ;Ji, Huizhong
通讯作者:Dong, Enqing
作者机构:[Xue, Peng ;Dong, Enqing ;Ji, Huizhong ] School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai; 264209, China
会议名称:23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
会议日期:24 July 2019 through 26 July 2019
来源:Communications in Computer and Information Science
出版年:2020
卷:1065 CCIS
页码:339-350
DOI:10.1007/978-3-030-39343-4_29
关键词:4D CT; Image registration; Markov Random Field; Topology preservation
摘要:Deformable image registration is an important tool in medical image analysis. In the case of lung four dimensions computed tomography (4D CT) registration, there is a major problem that the traditional image registration methods based on continuous optimization are easy to fall into the local optimal solution and lead to serious misregistration. In this study, we proposed a novel image registration method based on high-order Markov Random Fields (MRF). By analyzing the effect of the deformation field constraint of the potential functions with different order cliques in MRF model, energy functions with high-order cliques form are designed separately for 2D and 3D images to preserve the deformation field topology. For the complexity of the designed energy function with high-order cliques form, the Markov Chain Monte Carlo (MCMC) algorithm is used to solve the optimization problem of the designed energy function. To address the high computational requirements in lung 4D CT image registration, a multi-level processing strategy is adopted to reduce the space complexity of the proposed registration method and promote the computational efficiency. Compared with some other registration methods, the proposed method achieved the optimal average target registration error (TRE) of 0.93 mm on public DIR-lab dataset with 4D CT images, which indicates its great potential in lung motion modeling and image guided radiotherapy. © 2020, Springer Nature Switzerland AG.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079097931&doi=10.1007%2f978-3-030-39343-4_29&partnerID=40&md5=6b63b6483fd4216178bbb7cdd7e65011
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