C2FResMorph: A high-performance framework for unsupervised 2D medical image registration

Ding, Yi; Bu, Junjian; Qin, Zhen; You, Li; Cao, Mingsheng; Qin, Zhiguang; Pang, Minghui

doi:10.1016/j.patcog.2024.110615

Pattern Recognition

2024

DOI: 10.1016/j.patcog.2024.110615

|View full text |Cite

C2FResMorph: A high-performance framework for unsupervised 2D medical image registration

Yi Ding,

Junjian Bu,

Zhen Qin

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Robust and Efficient Registration of Infrared and Visible Images for Vehicular Imaging Systems

Che,

Lv,

Gong

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The automatic registration of infrared and visible images in vehicular imaging systems remains challenging in vision-assisted driving systems because of differences in imaging mechanisms. Existing registration methods often fail to accurately register infrared and visible images in vehicular imaging systems due to numerous spurious points during feature extraction, unstable feature descriptions, and low feature matching efficiency. To address these issues, a robust and efficient registration of infrared and visible images for vehicular imaging systems is proposed. In the feature extraction stage, we propose a structural similarity point extractor (SSPE) that extracts feature points using the structural similarity between weighted phase congruency (PC) maps and gradient magnitude (GM) maps. This approach effectively suppresses invalid feature points while ensuring the extraction of stable and reliable ones. In the feature description stage, we design a rotation-invariant feature descriptor (RIFD) that comprehensively describes the attributes of feature points, thereby enhancing their discriminative power. In the feature matching stage, we propose an effective coarse-to-fine matching strategy (EC2F) that improves the matching efficiency through nearest neighbor matching and threshold-based fast sample consensus (FSC), while improving registration accuracy through coordinate-based iterative optimization. Registration experiments on public datasets and a self-established dataset demonstrate the superior performance of our proposed method, and also confirm its effectiveness in real vehicular environments.

show abstract

Robust and Efficient Registration of Infrared and Visible Images for Vehicular Imaging Systems

Che,

Lv,

Gong

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

C2FResMorph: A high-performance framework for unsupervised 2D medical image registration

Cited by 1 publication

References 26 publications

Robust and Efficient Registration of Infrared and Visible Images for Vehicular Imaging Systems

Robust and Efficient Registration of Infrared and Visible Images for Vehicular Imaging Systems

Contact Info

Product

Resources

About