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• W4214736088 abstract "This paper describes a new non-rigid approach to register images from same- and cross-imaging modalities such as magnetic resonance imaging, computed tomography, and 3D rotational angiography. The deformation is a key challenge in medical image registration. We have proposed a diffeomorphism-based method to tackle this problem using an optimized framework. A non stationary velocity field is used to minimize the effect of forces that are derived from the image gradients. Furthermore, we propose a similarity energy function, based on the gray scale distribution, to limit the fluctuations while approaching the local minima. The proposed method is evaluated on both private and public datasets; the results show that the values of mean square error (MSE), normalized cross-correlation (NCC), structural similarity (SS), mutual information (MI), feature similarity index (FSIM), and mean absolute error (MAE) are 1.3136, 0.9962, 0.9897, 0.883, 0.9922, and 1.52± 2.09, respectively. Both qualitative and quantitative evaluation show promising registration accuracy reflecting the potential of the proposed method." @default.
• W4214736088 created "2022-03-02" @default.
• W4214736088 creator A5018877986 @default.
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• W4214736088 date "2022-01-01" @default.
• W4214736088 modified "2023-10-16" @default.
• W4214736088 title "Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration" @default.
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• W4214736088 doi "https://doi.org/10.1109/access.2022.3154771" @default.
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