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 Videos

A robust point-matching algorithm for autoradiograph alignment

A Rangarajan1, H Chui, E Mjolsness

  • 1Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520-8042, USA. anand@noodle.med.yale.edu

Medical Image Analysis
|January 5, 1999
PubMed
Summary
This summary is machine-generated.

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

Early to Mid-Holocene land use transitions in South Asia: A new archaeological synthesis of potential human impacts.

PloS one·2025
Same author

Domain general frontoparietal regions show modality-dependent coding of auditory and visual rules.

bioRxiv : the preprint server for biology·2024
Same author

3D Capsule Networks for Brain Image Segmentation.

AJNR. American journal of neuroradiology·2023
Same author

Feasibility of Passive ECG Bio-sensing and EMA Emotion Reporting Technologies and Acceptability of Just-in-Time Content in a Well-being Intervention, Considerations for Scalability and Improved Uptake.

Affective science·2022
Same author

We are what we (think we) eat: The effect of expected satiety on subsequent calorie consumption.

Appetite·2020
Same author

Urine dicarboxylic acids change in pre-symptomatic Alzheimer's disease and reflect loss of energy capacity and hippocampal volume.

PloS one·2020
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026
See all related articles

This study introduces a new automated method for aligning brain autoradiograph slices by matching point features and removing outliers. The technique accurately maps spatial differences, improving anatomical reconstruction for neuroscience research.

Area of Science:

  • Neuroimaging
  • Computational Anatomy
  • Medical Image Analysis

Background:

  • Autoradiographs are crucial for studying brain function and molecular distribution.
  • Accurate alignment of autoradiograph slices is essential for 3D reconstruction and analysis.
  • Existing methods may struggle with local variations and artifacts inherent in tissue slices.

Purpose of the Study:

  • To develop a novel, automated method for the geometric alignment of brain autoradiograph slices.
  • To accurately map spatial differences and correspondences between autoradiograph features.
  • To account for local, natural, and artifactual variations in brain tissue slices.

Main Methods:

  • Feature extraction and identification of point correspondences (homologies) between autoradiograph images.

Related Experiment Videos

  • Implementation of an outlier rejection mechanism to discard non-homologous points.
  • Application of an automated algorithm to align left prefrontal cortex autoradiograph slices.

    • Main Results:

    • Demonstrated the algorithm's ability to perform effective point outlier rejection.
    • Validated the robustness of the spatial mapping using synthetically generated data.
    • Provided a visual comparison with the Iterative Closest Point (ICP) algorithm, showing comparable or improved performance.

    Conclusions:

    • The novel method provides accurate geometric alignment of brain autoradiographs.
    • The automated approach effectively handles variations and artifacts in tissue slices.
    • This technique facilitates improved 3D reconstruction and analysis of brain structures from autoradiographic data.