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However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was simulated by rotational distortion and extrusion transform, medical image registration with large deformation was performed, and quantitative analyses were conducted using the registration evaluation indexes, and the influence of different driving forces and parameters on the registration result was analyzed. The registration results of same modality medical images were compared with those obtained using active demons, additive demons, and diffeomorphic demons. Quantitative analyses showed that the proposed method\u2019s normalized cross-correlation coefficient and structural similarity were the highest and mean square error was the lowest. Medical image registration with large deformation could be performed successfully; evaluation indexes remained stable with an increase in deformation strength. The proposed method is effective and robust, and it can be applied to nonrigid registration of same modality medical images with large deformation.<\/jats:p>","DOI":"10.1155\/2018\/7314612","type":"journal-article","created":{"date-parts":[[2018,5,16]],"date-time":"2018-05-16T20:13:50Z","timestamp":1526501630000},"page":"1-9","source":"Crossref","is-referenced-by-count":8,"title":["Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation"],"prefix":"10.1155","volume":"2018","author":[{"given":"Chang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Xinxiang Medical University, Xinxiang 453003, China"},{"name":"Key Lab of Neurosense and Control, Xinxiang Medical University, Xinxiang, Henan 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiongqiong","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Xinxiang Medical University, Xinxiang 453003, China"},{"name":"Key Lab of Neurosense and Control, Xinxiang Medical University, Xinxiang, Henan 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Xinxiang Medical University, Xinxiang 453003, China"},{"name":"Key Lab of Neurosense and Control, Xinxiang Medical University, Xinxiang, Henan 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-7113","authenticated-orcid":true,"given":"Yi","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Xinxiang Medical University, Xinxiang 453003, China"},{"name":"Key Lab of Neurosense and Control, Xinxiang Medical University, Xinxiang, Henan 453003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2003.816961"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/50\/12\/011"},{"key":"3","first-page":"597","volume":"1679","year":"1999","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2005.03.003"},{"issue":"2","key":"5","first-page":"319","volume":"10","year":"2007","journal-title":"Medical image computing and computer-assisted intervention : MICCAI ... 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