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

Skin cancer recognition by computer vision.

R H Moss1, W V Stoecker, S J Lin

  • 1Department of Electrical Engineering, University of Missouri, Rolla 65401.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|January 1, 1989
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

First Detection of Ultrahigh Energy Emission from Gamma-Ray Binary LS I +61° 303.

Physical review letters·2026
Same author

Evidence of Cosmic-Ray Acceleration up to Sub-PeV Energies in the Supernova Remnant IC 443.

Physical review letters·2026
Same author

Precise Measurement of the Cosmic Ray Helium Spectrum above 0.1 PeV.

Physical review letters·2026
Same author

All-Sky Search for Individual Primordial Black Hole Bursts with LHAASO.

Physical review letters·2025
Same author

[Interpretation of key updates in the "Consensus on the diagnosis and treatment of pituitary prolactinoma (2025 edition)"].

Zhonghua yi xue za zhi·2025
Same author

Measurement of Very-High-Energy Diffuse Gamma-Ray Emissions from the Galactic Plane with LHAASO-WCDA.

Physical review letters·2025
Same journal

Continual test-time adaptation via weight averaging of feature augmentations in cross-domain medical image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

A lightweight network for segmenting tree-like structures in medical images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Self-supervised isotropic reconstruction for abnormality detection in anisotropic MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

ScribSAM: A robust scribble-supervised framework for spatiotemporal segmentation of breast lesions in ultrasound videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
See all related articles

This study presents an automated method for detecting key features of basal cell epitheliomas using image analysis. The developed software aids in the diagnosis of skin tumors by dermatologists.

Area of Science:

  • Dermatology
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • Basal cell epithelioma (BCE) diagnosis relies on visual identification of specific features.
  • Accurate and timely diagnosis is crucial for effective treatment and patient outcomes.
  • Automated diagnostic tools can potentially improve efficiency and consistency in dermatological assessments.

Purpose of the Study:

  • To develop and evaluate an automated system for detecting characteristic features of basal cell epitheliomas from skin photographs.
  • To assess the feasibility of using image processing techniques for identifying semitranslucency, telangiectasia, ulcer, crust, and tumor border in skin lesions.
  • To integrate this image analysis software with the AI/DERM expert system for enhanced skin tumor diagnosis.

Main Methods:

Related Experiment Videos

  • Utilized image processing techniques including Fourier transform frequency analysis and the Sun-Wee texture analysis algorithm.
  • Applied various image analysis methods tailored for the examination of skin photographs.
  • Developed software specifically for integration with the AI/DERM expert system.

Main Results:

  • The study successfully demonstrated the automatic detection of several key features associated with basal cell epitheliomas.
  • Image analysis techniques were effective in identifying features such as semitranslucency, telangiectasia, ulcer, crust, and tumor border.
  • The developed software showed potential for aiding in the diagnostic process of skin tumors.

Conclusions:

  • Automated detection of basal cell epithelioma features is feasible using advanced image analysis techniques.
  • The developed software represents a promising tool for supporting dermatologists in skin tumor diagnosis.
  • Integration with expert systems like AI/DERM can enhance the capabilities of automated dermatological diagnostic tools.