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Journal of Medical Imaging

Deformable image registration for tissues with large displacements
Author(s): Xishi Huang; Jing Ren; Anwar Abdalbari; Mark Green
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Paper Abstract

Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients’ movements such as respiration and repositioning. In our previous work, we proposed a fast registration method for deformable tissues with small rotations. We extend our method to deformable registration of soft tissues with large displacements. We analyzed the deformation field of the liver by decomposing the deformation into shift, rotation, and pure deformation components and concluded that in many clinical cases, the liver deformation contains large rotations and small deformations. This analysis justified the use of linear elastic theory in our image registration method. We also proposed a region-based neuro-fuzzy transformation model to seamlessly stitch together local affine and local rigid models in different regions. We have performed the experiments on a liver MRI image set and showed the effectiveness of the proposed registration method. We have also compared the performance of the proposed method with the previous method on tissues with large rotations and showed that the proposed method outperformed the previous method when dealing with the combination of pure deformation and large rotations. Validation results show that we can achieve a target registration error of 1.87 ± 0.87    mm and an average centerline distance error of 1.28 ± 0.78    mm . The proposed technique has the potential to significantly improve registration capabilities and the quality of intraoperative image guidance. To the best of our knowledge, this is the first time that the complex displacement of the liver is explicitly separated into local pure deformation and rigid motion.

Paper Details

Date Published: 25 January 2017
PDF: 12 pages
J. Med. Imag. 4(1) 014001 doi: 10.1117/1.JMI.4.1.014001
Published in: Journal of Medical Imaging Volume 4, Issue 1
Show Author Affiliations
Xishi Huang, Istuary Innovation Group (Canada)
Jing Ren, Univ. of Ontario Institute of Technology (Canada)
Anwar Abdalbari, Univ. of Ontario Institute of Technology (Canada)
Mark Green, Univ. of Ontario Institute of Technology (Canada)


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