Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 11, 2026

Analyzing Dendritic Morphology in Columns and Layers

Analyzing Dendritic Morphology in Columns and Layers

Published on: March 23, 2017

Structure propagation for image registration.

Mehmet Yigitsoy¹, Nassir Navab

¹Department of Informatics, Technische Universität München, Munich, Germany. yigitsoy@in.tum.de

IEEE Transactions on Medical Imaging

|May 21, 2013

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

Erratum for: Associations of MRI-derived Paraspinal IMAT and LMM with Cardiometabolic Risk Factors: Results from a German Cohort.

Radiology·2026

Same author

ProtoFlow: interpretable and robust surgical workflow modeling with learned dynamic scene graph prototypes.

International journal of computer assisted radiology and surgery·2026

Same author

Toward comprehensive real-time scene understanding in ophthalmic surgery through multimodal image fusion.

International journal of computer assisted radiology and surgery·2026

Same author

DefSynUS: Real-time patient-specific intrahepatic vessel identification via deformation-aware CT-US domain adaptation.

International journal of computer assisted radiology and surgery·2026

Same author

Smartphone photogrammetry for rapid 3D surface modeling of head and neck specimens to support frozen section communication: A feasibility pilot study.

Oral oncology·2026

Same author

Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition·2026

Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026

Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026

Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026

Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026

Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026

Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026

See all related articles

This study introduces a novel medical image stitching method using perceptual grouping to extend subimages. This technique enables accurate alignment even with minimal or no overlap, ensuring structural continuity for improved large-field medical imaging.

Area of Science:

Medical Imaging
Computer Vision
Image Processing

Background:

Mosaicing stitches subimages for a larger field of view in medical imaging.
Registration is challenging with absent, small, or unreliable overlapping information.
Image artifacts and boundary distortions further complicate registration.

Purpose of the Study:

To propose a novel registration approach for stitching medical subimages in challenging scenarios.
To ensure continuity and smoothness of structures across subimage boundaries.
To enable contactless stitching and stitching with physical gaps.

Main Methods:

Utilizes a perceptual grouping approach to extend subimages beyond their boundaries.
Propagates available structures to create structural maps in extended regions.

More Related Videos

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Related Experiment Videos

Last Updated: May 11, 2026

Analyzing Dendritic Morphology in Columns and Layers

Analyzing Dendritic Morphology in Columns and Layers

Published on: March 23, 2017

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Establishes correspondences using these structural maps for alignment.

Main Results:

Demonstrates effective stitching of subimages even with absent, small, or unreliable overlap.
Ensures structural continuity and smoothness across stitched images.
Successfully performs contactless stitching and stitching across physical gaps.

Conclusions:

The novel approach effectively addresses limitations in current medical image stitching techniques.
The method ensures structural integrity and continuity, crucial for accurate medical diagnoses.
Applicable to multi-modal imaging and scenarios requiring contactless or gapped stitching.