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

United snakes.

Jianming Liang1, Tim McInerney, Demetri Terzopoulos

  • 1Computer Aided Diagnosis and Therapy, Siemens Medical Solutions USA, Inc., Malvern, PA 19355, USA. jianming.liang@computer.org

Medical Image Analysis
|November 29, 2005
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

Autodidactic dense anatomical models.

Medical image analysis·2026
Same author

MnUA-DOX-artesunate hydrogel remodels immunosuppressive tumor microenvironment and prevents postoperative recurrence in triple-negative breast cancer.

Journal of nanobiotechnology·2026
Same author

Allicin-based biomimetic nanoparticles of the erythrocyte membrane for the delivery of lumefantrine to enhance its antimalarial effect.

International journal of pharmaceutics: X·2026
Same author

Ark<sup>+</sup>: Supervised training a single high-performance AI foundation model from many differently labeled datasets-no label consolidation required.

Medical image analysis·2025
Same author

Astragaloside IV reduces hepatitis B surface antigen level via monocyte/macrophages in chronic HBV infection mice.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2025
Same author

A Neural Conditional Random Field Model Using Deep Features and Learnable Functions for End-to-End MRI Prostate Zonal Segmentation.

The journal of machine learning for biomedical imaging·2025
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

United Snakes unifies popular active contour models (snakes) and integrates them with live wire techniques for enhanced medical image analysis. This framework improves efficiency, reproducibility, and interactive control in segmentation tasks.

Area of Science:

  • Medical image analysis
  • Computer vision
  • Computational anatomy

Background:

  • Active contour models, known as snakes, have been a staple in image analysis since 1987.
  • Various snake implementations exist, including finite difference, B-spline, and Hermite polynomial variants.
  • Live wire (intelligent scissors) is a complementary technique often used in image segmentation.

Purpose of the Study:

  • To present a unified framework, "United Snakes", integrating popular snake variants.
  • To combine snakes with live wire techniques using a hard constraint mechanism.
  • To enhance efficiency, reproducibility, and user control in image analysis.

Main Methods:

  • Unified finite element formulation for finite difference, B-spline, and Hermite polynomial snakes.

Related Experiment Videos

  • Integration of snakes with live wire using an effective hard constraint mechanism.
  • Application to diverse medical image segmentation tasks.
  • Main Results:

    • Demonstrated generality and accuracy across multiple medical imaging applications.
    • Successfully segmented neuronal dendrites in EM images.
    • Enabled accurate quantification of growth plates in MR images and isolation of breast regions in mammograms.

    Conclusions:

    • The United Snakes framework offers a versatile and robust platform for medical image analysis.
    • Combining snakes and live wire techniques improves segmentation performance and user interaction.
    • The framework expands object modeling capabilities and minimizes user intervention.