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: Jun 13, 2026

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

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

Published on: July 26, 2014

Semi-supervised subspace learning for Mumford-Shah model based texture segmentation.

Yan Nei Law1, Hwee Kuan Lee, Andy M Yip

  • 1Imaging Informatics Group, Bioinformatics Institute, A*STAR, 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore.

Optics Express
|April 15, 2010
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

Epicardial adipose tissue signatures in Asian coronary artery disease: Insights from cardiac CT.

American journal of preventive cardiology·2026
Same author

DiffBulk: Enhancing Spatial Transcriptomic Prediction with Diffusion-Based Training.

IEEE transactions on medical imaging·2026
Same author

Can 3D T1 Post-Contrast MRI in A Radiomics-Machine Learning Model Distinguish Infective from Neoplastic Ring-Enhancing Brain Lesions? An Exploratory Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study.

European heart journal. Digital health·2025
Same author

Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability.

Journal of cheminformatics·2025
Same author

OAH-Net: a deep neural network for efficient and robust hologram reconstruction for off-axis digital holographic microscopy.

Biomedical optics express·2025
Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026
Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026
Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026
Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026
Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026
Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026
See all related articles

This study introduces a new semi-supervised method for image texture segmentation. It combines subspace clustering with the Mumford-Shah model to improve segmentation accuracy, especially for challenging textures.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Image segmentation is crucial for analyzing visual data.
  • Traditional Mumford-Shah models struggle with complex texture segmentation.
  • Unsupervised subspace learning can lead to suboptimal solutions.

Purpose of the Study:

  • To develop a novel image segmentation model for texture analysis.
  • To overcome limitations of unsupervised subspace learning in segmentation.
  • To enhance the Mumford-Shah model with semi-supervised subspace clustering.

Main Methods:

  • Incorporation of subspace clustering techniques into the Mumford-Shah model.
  • Development of a semi-supervised optimization algorithm using intermediate results and user-defined regions-of-interest (ROI).

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jun 13, 2026

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

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

Published on: July 26, 2014

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

  • Embedding optimal subspaces into the Mumford-Shah objective function for subspace-homogeneous segments.

    • Main Results:

    • The proposed model outperforms standard Mumford-Shah models in texture segmentation.
    • Effective separation of textures that are poorly distinguished in the full feature space.
    • Demonstrated usefulness of subspace clustering for texture segmentation through experimental validation.

    Conclusions:

    • The novel semi-supervised approach significantly improves texture segmentation.
    • Subspace clustering provides a powerful tool for enhancing image segmentation models.
    • The method offers a robust solution for complex texture analysis tasks.