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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Vertebrae, IVD and spinal canal boundary extraction on MRI, utilizing CT-trained active shape models.

International journal of computer assisted radiology and surgery·2021
Same author

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

Advances in experimental medicine and biology·2020
Same author

Drugena: A Fully Automated Immunoinformatics Platform for the Design of Antibody-Drug Conjugates Against Neurodegenerative Diseases.

Advances in experimental medicine and biology·2020
Same author

MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU.

IEEE journal of biomedical and health informatics·2016
Same author

A custom grow-cut based scheme for 2D-gel image segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016
Same author

Grow-cut based automatic cDNA microarray image segmentation.

IEEE transactions on nanobioscience·2014

Related Experiment Video

Updated: Apr 25, 2026

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

3.6K

Self-parameterized active contours based on regional edge structure for medical image segmentation.

Eleftheria A Mylona1, Michalis A Savelonas1, Dimitris Maroulis1

  • 1Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15703 Panepistimiopolis, Athens Greece.

Springerplus
|August 26, 2014
PubMed
Summary

This study presents an automated method for medical image segmentation, eliminating manual parameter tuning. The novel framework ensures high-quality segmentation results objectively and efficiently.

Keywords:
Active contoursMedical image segmentationStructure tensorsUnsupervised parameterization

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.7K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.5K

Related Experiment Videos

Last Updated: Apr 25, 2026

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

3.6K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.7K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.5K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Computational Anatomy

Background:

  • Medical image segmentation is crucial for diagnosis and treatment planning.
  • Manual parameterization of active contour models is time-consuming and subjective.
  • Existing methods often require extensive empirical tuning by experts.

Purpose of the Study:

  • To introduce a novel unsupervised framework for active contour parameterization.
  • To automate the parameterization of regularization and data fidelity terms.
  • To improve the objectivity and efficiency of medical image segmentation.

Main Methods:

  • Developed a framework inspired by the isomorphism between structure tensor eigenvalues and active contour parameters.
  • Applied the framework to region-based active contour models for medical image segmentation.
  • Utilized eigenvalues as descriptors for orientation coherence in edge-containing regions.

Main Results:

  • Demonstrated high segmentation quality without manual parameter adjustment.
  • The proposed framework effectively automates parameterization.
  • Reduced the need for laborious, time-consuming trial-and-error tuning.

Conclusions:

  • The novel framework successfully automates active contour parameterization for medical image segmentation.
  • This approach enhances segmentation objectivity and efficiency for medical doctors.
  • The method achieves high segmentation quality consistently.