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

Postural Analysis in Temporomandibular Joint Dysfunction (TMJD) Patients: Correlation With Head and Neck Positioning.

International journal of dentistry·2026
Same author

Diagnostic performance of LINE-1 repetitive elements in stool DNA for non-invasive detection of colorectal cancer.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2026
Same author

Palatal malignant peripheral nerve sheath tumor: A case report and review of literature.

Rare tumors·2026
Same author

Fluorescence-Enhanced Catalytic Hairpin Assembly-Driven Nanobiosensor for Ultrasensitive Detection of HPV16 E7 mRNA.

Journal of medical virology·2026
Same author

A comprehensive review of the emerging role of NRF3 in ovarian cancer tumorigenesis and progression.

Journal of ovarian research·2026
Same author

Correction: Clinical implications of Alu‑based cell‑free DNA and serum onco‑piRNA monitoring in colorectal cancer management.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2025

Related Experiment Video

Updated: Mar 19, 2026

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.9K

Liver segmentation with new supervised method to create initial curve for active contour.

Abouzar Zareei1, Abbas Karimi1

  • 1Department of Computer Engineering, Faculty of Engineering, Arak Branch, Islamic Azad University, Arak, Markazi, Iran.

Computers in Biology and Medicine
|June 11, 2016
PubMed
Summary

This study introduces an improved Active Contour Model (ACM) for accurate liver segmentation in CT scans. The method enhances liver disease detection by overcoming limitations of traditional ACMs, improving diagnostic accuracy.

Keywords:
Active Contour Model (ACM)Initial contourLiver segmentation

More Related Videos

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor
06:39

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor

Published on: May 23, 2025

533
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

3.7K

Related Experiment Videos

Last Updated: Mar 19, 2026

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.9K
Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor
06:39

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor

Published on: May 23, 2025

533
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

3.7K

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Radiology

Background:

  • Liver diseases pose a significant health burden, necessitating accurate detection and treatment strategies.
  • Accurate liver segmentation is crucial for identifying liver diseases and tumors.
  • Traditional Active Contour Models (ACM) are sensitive to initial parameters and prone to local minima.

Purpose of the Study:

  • To develop an improved Active Contour Model (ACM) for accurate liver segmentation in CT images.
  • To enhance the robustness of ACM by incorporating Gradient Vector Flow (GVF) and balloon energy.
  • To optimize ACM parameters using a genetic algorithm for improved performance.

Main Methods:

  • A novel image energy-based pre-processing method was used to obtain initial liver segmentation.
  • An enhanced ACM incorporating GVF and balloon energy was implemented to refine segmentation.
  • A genetic algorithm was employed to optimize ACM control parameters.
  • The proposed method was evaluated on Sliver CT image datasets.

Main Results:

  • The enhanced ACM demonstrated high accuracy, precision, sensitivity, and specificity.
  • The method achieved low overlap error, Mean Squared Difference (MSD), and reduced runtime.
  • The proposed approach required fewer ACM iterations for effective segmentation.
  • The pre-processing method showed superior liver tissue segmentation capabilities in a shorter time.

Conclusions:

  • The proposed method offers a robust and accurate approach for liver segmentation in CT images.
  • The integration of GVF, balloon energy, and genetic algorithms overcomes key ACM limitations.
  • This technique holds promise for improving the early detection and treatment planning of liver diseases.