Jingya Zhang1,2, Jiajun Wang3, Xiuying Wang4, Dagan Feng5. 1. School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, People's Republic of China. zhangjy0611@163.com. 2. Department of Physics, Changshu Institute of Technology, Changshu, 215500, People's Republic of China. zhangjy0611@163.com. 3. School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, People's Republic of China. jjwang@suda.edu.cn. 4. Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney, NSW, 2006, Australia. xiu.wang@sydney.edu.au. 5. Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney, NSW, 2006, Australia. dagan.feng@sydney.edu.au.
Abstract
PURPOSE: Nonrigid registration of multimodal medical images remains a challenge in image-guided interventions. A common approach is to use mutual information (MI), which is robust to the intensity variations across modalities. However, primarily based on intensity distribution, MI does not take into account of underlying spatial and structural information of the images, which might lead to local optimization. To address such a challenge, this paper proposes a two-stage multimodal nonrigid registration scheme with joint structural information and local entropy. METHODS: In our two-stage multimodal nonrigid registration scheme, both the reference image and floating image are firstly converted to a common space. A unified representation in the common space for the images is constructed by fusing the structure tensor (ST) trace with the local entropy (LE). Through the representation that reflects its geometry uniformly across modalities, the complicated deformation field is estimated using L(1) or L(2) distance. RESULTS: We compared our approach to four other methods: (1) the method using LE, (2) the method using ST, (3) the method using spatially weighted LE and (4) the conventional MI-based method. Quantitative evaluations on 80 multimodal image pairs of different organs including 50 pairs of MR images with artificial deformations, 20 pairs of medical brain MR images and 10 pairs of breast images showed that our proposed method outperformed the comparison methods. Student's t test demonstrated that our method achieved statistically significant improvement on registration accuracy. CONCLUSION: The two-stage registration with joint ST and LE outperformed the conventional MI-based method for multimodal images. Both the ST and the LE contributed to the improved registration accuracy.
PURPOSE: Nonrigid registration of multimodal medical images remains a challenge in image-guided interventions. A common approach is to use mutual information (MI), which is robust to the intensity variations across modalities. However, primarily based on intensity distribution, MI does not take into account of underlying spatial and structural information of the images, which might lead to local optimization. To address such a challenge, this paper proposes a two-stage multimodal nonrigid registration scheme with joint structural information and local entropy. METHODS: In our two-stage multimodal nonrigid registration scheme, both the reference image and floating image are firstly converted to a common space. A unified representation in the common space for the images is constructed by fusing the structure tensor (ST) trace with the local entropy (LE). Through the representation that reflects its geometry uniformly across modalities, the complicated deformation field is estimated using L(1) or L(2) distance. RESULTS: We compared our approach to four other methods: (1) the method using LE, (2) the method using ST, (3) the method using spatially weighted LE and (4) the conventional MI-based method. Quantitative evaluations on 80 multimodal image pairs of different organs including 50 pairs of MR images with artificial deformations, 20 pairs of medical brain MR images and 10 pairs of breast images showed that our proposed method outperformed the comparison methods. Student's t test demonstrated that our method achieved statistically significant improvement on registration accuracy. CONCLUSION: The two-stage registration with joint ST and LE outperformed the conventional MI-based method for multimodal images. Both the ST and the LE contributed to the improved registration accuracy.
Entities:
Keywords:
Local entropy; Mutual information; Nonrigid registration; Structure tensor
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