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

Related Concept Videos

Routh-Hurwitz Criterion I

Routh-Hurwitz Criterion I

Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...

Routh-Hurwitz Criterion II

Routh-Hurwitz Criterion II

In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...

You might also read

Related Articles

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

Sort by

Same author

Intensive Care Unit Admissions Following Enhanced Recovery After Surgery for Laparoscopic Repair of Perforated Peptic Ulcer.

Journal of investigative surgery : the official journal of the Academy of Surgical Research·2026

Same author

Personalized Network-Guided Neuromodulation Enhances Human Working Memory.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Laparoscopic radical proximal gastrectomy with double flap technique for early proximal gastric cancer: a prospective, phase II study.

Frontiers in oncology·2026

Same author

Training sparse convolutional deep predictive coding networks with attention.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Hydrostructural and dynamic characteristics of compacted Nanning red clay considering wetting-drying impacts.

Scientific reports·2026

Same author

Spatial heterogeneity and subtypes of functional connectivity development in youth.

Nature communications·2026

Same journal

From Geometry to Intensity: A Coarse-to-Fine Pipeline for Unsupervised 3D Ultrasound Stitching.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 24, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Diffeomorphic image registration with bijective consistency.

Jiong Wu¹, Hongming Li¹, Yong Fan¹

¹Center for AI and Data Science for Integrated Diagnostics, Center for Biomedical Image, Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

Proceedings of Spie--The International Society for Optical Engineering

|March 5, 2025

Summary

This summary is machine-generated.

This study introduces a new multi-level framework for diffeomorphic image registration, improving accuracy and topology preservation. It ensures bijective consistency for more reliable spatial transformations in medical imaging.

Keywords:

Bijective consistency Convolutional neural networks Diffeomorphic image registration Unsupervised learning

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Related Experiment Videos

Last Updated: May 24, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Area of Science:

Medical Image Analysis
Computational Anatomy
Machine Learning in Medical Imaging

Background:

Unsupervised learning methods show potential for diffeomorphic image registration.
Bijective consistency of spatial transformations remains under-investigated in current registration studies.

Purpose of the Study:

To develop a multi-level framework for coarse-to-fine diffeomorphic image registration.
To enhance bijective consistency of spatial transformations in image registration.

Main Methods:

Proposed a novel stationary velocity field computation integrating forward and inverse fields for order invariance.
Introduced a new bijective consistency regularization to enforce consistent forward and inverse transformations.
Validated the framework on T1-weighted MRI brain datasets.

Main Results:

The proposed method achieved superior image registration accuracy compared to state-of-the-art methods.
Demonstrated enhanced topology preserving performance in diffeomorphic registration.
The registration results were invariant to the order of input images.

Conclusions:

The multi-level framework effectively achieves accurate and topologically consistent diffeomorphic image registration.
The novel methods for stationary velocity field computation and bijective consistency regularization advance the field of medical image registration.