Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 2, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

8.7K

A marker-free automatic alignment method based on scale-invariant features.

Renmin Han1, Fa Zhang2, Xiaohua Wan2

  • 1Key Lab of Intelligent Information Processing and Advanced Computing Research Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, China.

Journal of Structural Biology
|March 4, 2014
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same author

DyMamba: dynamic Mamba for microscopy image semantic segmentation.

Bioinformatics (Oxford, England)·2026
Same author

Earthworm-Inspired Self-Powered Multistimuli Neuromorphic Vision Skin with Homogeneous Ion Heterogel Arrays.

ACS applied materials & interfaces·2026
Same author

A variational framework with composite sparse regularization for cryo-electron tomography reconstruction.

Bioinformatics (Oxford, England)·2026
Same author

Corrective osteotomy for distal radius malunion using 3D-printed patient-specific guides and spacers: a retrospective comparative study.

BMC musculoskeletal disorders·2026
Same author

MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals.

IEEE journal of biomedical and health informatics·2026
Same journal

MLAC: MicroED-assisted ligand structure analysis in complexes and its application to hERG-ligand complexes.

Journal of structural biology·2026
Same journal

Ultrastructural evidence of autophagy-related processes and mitochondrial remodeling in the myxozoan parasite Henneguya piaractus.

Journal of structural biology·2026
Same journal

Architecture and dynamics of a supramolecular oxygen transport system in human homogentisate 1,2-Dioxygenase.

Journal of structural biology·2026
Same journal

Connecting pathways between mineralized fibrocartilage and bone at the Achilles tendon insertion.

Journal of structural biology·2026
Same journal

Structural and functional characterization of thermostable EstS1 esterase for BHET degradation.

Journal of structural biology·2026
Same journal

Following the white rabbit: multiscale 2D3D correlative imaging of bone structure.

Journal of structural biology·2026
See all related articles
This summary is machine-generated.

This study introduces a novel marker-free alignment method for electron tomography using Scale-Invariant Feature Transform (SIFT). The new approach achieves alignment accuracy comparable to fiducial marker methods, enabling higher resolution reconstructions.

Area of Science:

  • Microscopy
  • Image Processing
  • Structural Biology

Background:

  • Accurate alignment of tilt series is crucial for high-resolution reconstruction in electron tomography.
  • Automatic alignment without fiducial markers presents a significant challenge in the field.

Purpose of the Study:

  • To develop and evaluate a novel marker-free alignment method for electron tomography using Scale-Invariant Feature Transform (SIFT).
  • To improve the accuracy and resolution of tomographic reconstructions by addressing the limitations of current alignment techniques.

Main Methods:

  • The proposed method utilizes Scale-Invariant Feature Transform (SIFT) for detecting and localizing interest points (features).
  • It incorporates a reliable feature matching and tracking strategy to generate numerous feature tracks.
Keywords:
AlignmentBundle adjustmentElectron tomographySIFT

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

335
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

14.1K

Related Experiment Videos

Last Updated: May 2, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

8.7K
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

335
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

14.1K
  • An incremental bundle adjustment approach is employed for robust optimization of projection parameters, tolerating noise.
  • Main Results:

    • The marker-free alignment method demonstrated improved accuracy, achieving results comparable to traditional fiducial marker-based alignment.
    • Experimental data evaluation confirmed the effectiveness of the SIFT-based approach.
    • The enhanced alignment accuracy led to a subsequent increase in the resolution of the obtained tomograms.

    Conclusions:

    • The proposed SIFT-based marker-free alignment method offers a viable and effective alternative for electron tomography.
    • This technique enhances alignment accuracy and facilitates higher resolution tomographic reconstructions.
    • It addresses a key challenge in automated electron tomography, potentially broadening its applicability.