Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration
<p>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 diffeomorphis...
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2022
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| _version_ | 1864513562686259200 |
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| author | Snigdha Mohanty (16904919) |
| author2 | Sarada Prasad Dakua (14151789) |
| author2_role | author |
| author_facet | Snigdha Mohanty (16904919) Sarada Prasad Dakua (14151789) |
| author_role | author |
| dc.creator.none.fl_str_mv | Snigdha Mohanty (16904919) Sarada Prasad Dakua (14151789) |
| dc.date.none.fl_str_mv | 2022-02-25T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3154771 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Toward_Computing_Cross-Modality_Symmetric_Non-Rigid_Medical_Image_Registration/24056496 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning Strain Image registration Computed tomography Neural networks Magnetic resonance imaging Deformable models Three-dimensional displays Diffeomorphism MRI CT |
| dc.title.none.fl_str_mv | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3154771" target="_blank">https://dx.doi.org/10.1109/access.2022.3154771</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_02b0d6c181a54a69c23eeae9ab4a2517 |
| identifier_str_mv | 10.1109/access.2022.3154771 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056496 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image RegistrationSnigdha Mohanty (16904919)Sarada Prasad Dakua (14151789)Biomedical and clinical sciencesClinical sciencesEngineeringBiomedical engineeringInformation and computing sciencesMachine learningStrainImage registrationComputed tomographyNeural networksMagnetic resonance imagingDeformable modelsThree-dimensional displaysDiffeomorphismMRICT<p>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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3154771" target="_blank">https://dx.doi.org/10.1109/access.2022.3154771</a></p>2022-02-25T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3154771https://figshare.com/articles/journal_contribution/Toward_Computing_Cross-Modality_Symmetric_Non-Rigid_Medical_Image_Registration/24056496CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240564962022-02-25T00:00:00Z |
| spellingShingle | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration Snigdha Mohanty (16904919) Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning Strain Image registration Computed tomography Neural networks Magnetic resonance imaging Deformable models Three-dimensional displays Diffeomorphism MRI CT |
| status_str | publishedVersion |
| title | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration |
| title_full | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration |
| title_fullStr | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration |
| title_full_unstemmed | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration |
| title_short | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration |
| title_sort | Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration |
| topic | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning Strain Image registration Computed tomography Neural networks Magnetic resonance imaging Deformable models Three-dimensional displays Diffeomorphism MRI CT |