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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...

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Unsupervised skin lesions border detection via two-dimensional image analysis.

Qaisar Abbas1, Irene Fondón, Muhammad Rashid

  • 1Department of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China. qaisarabbasphd@gmail.com

Computer Methods and Programs in Biomedicine
|July 29, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an unsupervised method for segmenting skin lesions in dermoscopic images, improving automated melanoma detection. The approach enhances true detection rates and reduces false positives for better skin cancer analysis.

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Vision

Background:

  • Dermoscopy is crucial for analyzing skin cancer, aiding dermatologists in classifying tumors like melanoma and carcinoma.
  • Accurate automated border detection is vital for reliable computerized melanoma recognition systems.
  • Dermoscopic images often contain artifacts (e.g., gel, reflections, skin features) that can impede analysis.

Purpose of the Study:

  • To develop an unsupervised approach for segmenting multiple skin lesions in dermoscopic images.
  • To modify Region-based Active Contours (RACs) and implement artifact diminution techniques.
  • To improve the accuracy of automated border detection in dermoscopic images for skin cancer analysis.

Main Methods:

  • An unsupervised method combining modified Region-based Active Contours (RACs) and artifact reduction was employed.
  • Iterative thresholding was used for automatic level set initialization, with Courant-Friedfriedrichs-Lewy (CFL) function constraints for curve stability.
  • The system was tested on 320 dermoscopic images, with border detection error quantified using five statistical metrics against dermatologist-annotated ground truth.

Main Results:

  • The unsupervised border detection system achieved a 4.31% increase in true detection rate (TDR).
  • The system demonstrated a 5.28% reduction in false positive rate (FPR).
  • Segmentation results showed competitive performance compared to state-of-the-art methods.

Conclusions:

  • The proposed unsupervised approach effectively segments skin lesions and reduces artifacts in dermoscopic images.
  • This method enhances the accuracy of automated border detection, crucial for reliable skin cancer diagnosis.
  • The improved true detection rate and reduced false positive rate signify a step forward in computer-aided melanoma recognition.