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ORIGINAL ARTICLE
Year : 2017  |  Volume : 3  |  Issue : 4  |  Page : 178-185

Nonrigid registration of multimodal medical images based on hybrid model


1 Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic Engineering, Xidian University, Xi'an, Shaanxi Province, China
2 Department of Radiation Oncology, University of California, Los Angeles, California, USA

Correspondence Address:
Shuiping Gou
No. 2 South Taibai Road, Xi'an 710071, Shaanxi Province
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/digm.digm_39_17

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Background and Objectives: Multimodal image registration is a crucial step in prostate cancer radiation therapy scheme. However, it can be challenging due to the obvious appearance difference between computed tomography (CT) and magnetic resonance imaging (MRI) and unavoidable organ motion. Accordingly, a nonrigid registration framework for precisely registering multimodal prostate images is proposed in this paper. Materials and Methods: In this work, multimodal prostate image registration between CT and MRI is achieved using a hybrid model that integrates multiresolution strategy and Demons algorithm. Furthermore, to precisely describe the deformation of prostate, B-spline-based registration is utilized to refine the initial registration result of multiresolution Demons algorithm. Results: To evaluate our method, experiments on clinical prostate data sets of nine participants and comparison with the conventional Demons algorithm are conducted. Experimental results demonstrate that the proposed registration method outperforms the Demons algorithm by a large margin in terms of mutual information and correlation coefficient. Conclusions: These results show that our method outperforms the Demons algorithm and can achieve excellent performance on multimodal prostate images even the appearances of prostate change significantly. In addition, the results demonstrate that the proposed method can help to localize the prostate accurately, which is feasible in clinical.


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