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Multimodal image registration with joint structure tensor and local entropy

  • Jingya Zhang
  • Jiajun WangEmail author
  • Xiuying Wang
  • Dagan Feng
Original Article

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.

Keywords

Nonrigid registration Structure tensor Local entropy Mutual information 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China, No. 60871086 and Natural Science Foundation of Jiangsu Province China, No. BK2008159.

Conflict of interest

None.

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Copyright information

© CARS 2015

Authors and Affiliations

  • Jingya Zhang
    • 1
    • 2
  • Jiajun Wang
    • 1
    Email author
  • Xiuying Wang
    • 3
  • Dagan Feng
    • 3
  1. 1.School of Electronic and Information EngineeringSoochow UniversitySuzhouPeople’s Republic of China
  2. 2.Department of PhysicsChangshu Institute of TechnologyChangshuPeople’s Republic of China
  3. 3.Biomedical and Multimedia Information Technology Research Group, School of Information TechnologiesUniversity of SydneySydneyAustralia

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