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Deformable image registration based on elastodynamics

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Abstract

This paper presents a novel method for 2D inter-subject non-rigid image registration. It is motivated by the ideas derived from elastodynamics which is the subclass of linear elastic theory. We propose to model the non-rigid deformations as elastic waves which are characterized by elastodynamics wave equation. Our registration method is formulated in a multi-resolution manner such that it is able to recover significant deformations. The proposed scheme recovers local (non-linear) as well as global deformations. The results of image registration by the proposed technique were validated via similarity measures for real MR brain images, BrainWeb dataset and MR prostate images. We have also used overlap measures (Jaccard distance, Dice coefficient and Overlap coefficient) over the segmented deep-brain structures including: lateral and third ventricles, putamen and caudate nucleus for real image dataset to further validate our proposed registration scheme. The proposed scheme was also compared with demons, Level set and free-form deformations (cubic B-spline) algorithms implemented in ITK toolkit and symmetric normalization method available in ANTs software package. Our proposed algorithm shows marked improvement of the inter-subject non-rigid registration, both qualitatively and quantitatively, over the four above-mentioned methods. This comparison was performed on all the three types of datasets mentioned above. 2D synthetic experiments comprising of Patch-‘C’, Square–Rectangle and Circle-‘C’ were also performed with our algorithm and compared with other four methods. Both qualitative and quantitative results show that our method performs better than the other four methods. Results suggest that our proposed algorithm has improved accuracy over lemons, level set and free-form deformations (ITK implemented) and symmetric normalization method (ANTs based), tested over two types of brain and one prostate MRI. The commonly used synthetic experiments also suggest the superiority of our proposed scheme.

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Notes

  1. available online: https://imaging.nci.nih.gov/ncia/login.jsf.

  2. These ranges are determined using cross-validation method.

  3. available online at: http://picsl.upenn.edu/software/ants/.

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  1. National University of Sciences and Technology (NUST), College of Telecommunication Engineering, Islamabad, Pakistan

    Sahar Ahmad & Muhammad Faisal Khan

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  1. Sahar Ahmad
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  2. Muhammad Faisal Khan
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Correspondence to Sahar Ahmad.

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Ahmad, S., Khan, M.F. Deformable image registration based on elastodynamics. Machine Vision and Applications 26, 689–710 (2015). https://doi.org/10.1007/s00138-015-0690-1

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