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

Skin Cancer01:30

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

You might also read

Related Articles

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

Sort by
Same author

A scalable exemplar-based method for aligning biological taxonomies.

Biodiversity data journal·2026
Same author

Electrophysiological Brain Connectivity and Subjective States Evoked by Electrical Stimulation of the Human Mid-thalamus.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

Response: Lenses protecting against photosensitivity violate international driving regulations.

Epilepsia·2026
Same author

Human Mediodorsal Thalamus in Seizure Propagation.

bioRxiv : the preprint server for biology·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Electrophysiological Brain Connectivity and Subjective States Evoked by Electrical Stimulation of the Human Mediodorsal Thalamus.

bioRxiv : the preprint server for biology·2025
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Depth data improves skin lesion segmentation.

Xiang Li1, Ben Aldridge, Lucia Ballerini

  • 1School of Informatics, University of Edinburgh, UK. x.li-29@sms.ed.ac.uk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 30, 2010
PubMed
Summary
This summary is machine-generated.

Adding 3D depth information to skin lesion images significantly enhances segmentation accuracy. This method integrates color and depth data for more precise identification of pigmented and non-pigmented lesions.

More Related Videos

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Related Experiment Videos

Last Updated: Jun 13, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate segmentation of skin lesions is crucial for diagnosis and treatment.
  • Existing methods using only RGB color images have limitations in differentiating certain lesion types.
  • Integrating multi-modal data can potentially improve segmentation performance.

Purpose of the Study:

  • To evaluate the impact of 3D depth information on skin lesion segmentation.
  • To develop and present a novel region-based active contour segmentation approach.
  • To compare the discriminative power of color, depth, and texture properties.

Main Methods:

  • A region-based active contour segmentation model utilizing a level-set framework.
  • Integration of chromatic (color) and geometric (depth) information.
  • Statistical modeling for analyzing lesion properties.

Main Results:

  • The proposed method integrating chromatic and geometric information improved segmentation accuracy for both pigmented and non-pigmented skin lesions.
  • Segmentation results for pigmented lesions closely matched dermatologists' assessments.
  • More consistent and accurate segmentation was achieved for non-pigmented lesions compared to methods using only color information.

Conclusions:

  • 3D depth information is a valuable addition to RGB color images for enhanced skin lesion segmentation.
  • The proposed level-set based active contour method effectively integrates multi-modal data for improved accuracy.
  • This approach offers a more reliable tool for the analysis of skin lesions, aiding dermatological assessments.