文献类型：期刊文献

中文题名：Deformable Registration Algorithm via Non-subsampled Contourlet Transform and Saliency Map

英文题名：Deformable Registration Algorithm via Non-subsampled Contourlet Transform and Saliency Map

作者：Chang, Qing[1]; Yang, Wenyou[1]; Chen, Lanlan[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2022

卷号：27

期号：4

起止页码：452

中文期刊名：Journal of Shanghai Jiaotong university(Science)

外文期刊名：Journal of Shanghai Jiaotong University (Science)

收录：EI(收录号：20222112130108);Scopus;PubMed

基金：the National Natural Science Foundation of China(No.61976091)。

语种：英文

中文关键词：medical image registration;non-subsampled contourlet transform;saliency map;Markov random fields

外文关键词：Image registration - Medical imaging - Deformation - Markov processes - Image enhancement

摘要：Medical image registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondence between planning and treatment images.However,most methods based on intensity have the problems of matching ambiguity and ignoring the influence of weak correspondence areas on the overall registration.In this study,we propose a novel general-purpose registration algorithm based on free-form deformation by non-subsampled contourlet transform and saliency map,which can reduce the matching ambiguities and maintain the topological structure of weak correspondence areas.An optimization method based on Markov random fields is used to optimize the registration process.Experiments on four public datasets from brain,cardiac,and lung have demonstrated the general applicability and the accuracy of our algorithm compared with two state-of-the-art methods.
Medical image registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondence between planning and treatment images. However, most methods based on intensity have the problems of matching ambiguity and ignoring the influence of weak correspondence areas on the overall registration. In this study, we propose a novel general-purpose registration algorithm based on free-form deformation by non-subsampled contourlet transform and saliency map, which can reduce the matching ambiguities and maintain the topological structure of weak correspondence areas. An optimization method based on Markov random fields is used to optimize the registration process. Experiments on four public datasets from brain, cardiac, and lung have demonstrated the general applicability and the accuracy of our algorithm compared with two state-of-the-art methods. ? 2022, Shanghai Jiao Tong University.