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

You might also read

Related Articles

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

Sort by

Same author

Oridonin protects the lung against hyperoxia-induced injury in a mouse model.

Undersea & hyperbaric medicine : journal of the Undersea and Hyperbaric Medical Society, Inc·2017

Same author

Changes in angiotensin II and angiotensin-converting enzyme of different tissues after prolonged hyperoxia exposure.

Undersea & hyperbaric medicine : journal of the Undersea and Hyperbaric Medical Society, Inc·2017

Same author

Sarcomatoid Renal Cell Carcinoma Has a Distinct Molecular Pathogenesis, Driver Mutation Profile, and Transcriptional Landscape.

Clinical cancer research : an official journal of the American Association for Cancer Research·2017

Same author

Cell type-selective imaging and profiling of newly synthesized proteomes by using puromycin analogues.

Chemical communications (Cambridge, England)·2017

Same author

A cheat sheet to navigate the complex maze of pharmaceutical exclusivities in Europe.

Pharmaceutical patent analyst·2017

Same author

Hydroxamic Acids as Chemoselective (ortho-Amino)arylation Reagents via Sigmatropic Rearrangement.

Angewandte Chemie (International ed. in English)·2017

Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 1, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Automatic image segmentation by dynamic region merging.

Bo Peng¹, Lei Zhang, David Zhang

¹Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 26, 2011

Summary

This summary is machine-generated.

This study introduces a novel dynamic region-merging algorithm for automatic image segmentation. It efficiently merges image regions using a statistical test, improving segmentation accuracy and speed for natural images.

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jun 1, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Automatic image segmentation is crucial for image analysis.
Existing region-merging algorithms face challenges in determining merging order and stopping criteria.

Purpose of the Study:

To develop a novel dynamic region-merging algorithm for automatic image segmentation.
To address the challenges of merging order and stopping criteria in region-merging algorithms.

Main Methods:

An oversegmented image is iteratively refined by merging homogeneous regions based on a statistical test.
A novel predicate, combining sequential probability ratio test and minimal cost criterion, guides the merging process.
The merging order is optimized using dynamic programming principles, formulating segmentation as an inference problem.

Main Results:

The proposed algorithm effectively solves the merging order and stopping criterion problems.
The dynamic region-merging approach ensures global properties of the final segmentation.
A faster algorithm utilizing a nearest neighbor graph accelerates the merging process.

Conclusions:

The dynamic region-merging algorithm provides an effective solution for automatic image segmentation.
The method demonstrates robust performance on real natural images.
This approach offers a principled way to perform image segmentation as an inference problem.