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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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Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus
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Lesion border detection in dermoscopy images using dynamic programming.

Qaisar Abbas1, M Emre Celebi, Irene Fondón García

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

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)
|January 14, 2011
PubMed
Summary
This summary is machine-generated.

This study presents an automated method for artifact removal and lesion border detection in dermoscopy images, achieving high accuracy for various skin lesion types. The combined approach enhances diagnostic sensitivity and specificity.

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Area of Science:

  • Medical image analysis
  • Dermatology
  • Computer vision

Background:

  • Automated border detection in dermoscopy is challenging due to image artifacts.
  • Artifacts include illumination, gel, hair, skin lines, and blood vessels.
  • Robust methods are needed for artifact removal and lesion border detection.

Purpose of the Study:

  • To develop and evaluate an automated method for artifact removal and lesion border detection in dermoscopy images.
  • To improve the accuracy of skin lesion segmentation.

Main Methods:

  • A three-step automated method: preprocessing for artifact reduction, edge candidate point detection using least-squares method (LSM), and tumor outline delineation via dynamic programming (DP).
  • Performance evaluated against dermatologist-drawn borders as ground truth.

Main Results:

  • The method was tested on 240 dermoscopic images across various skin lesion types (melanoma, BCC, SK, etc.).
  • Achieved mean border detection errors ranging from 2.01% to 9.0% for different lesion types.
  • Demonstrated low error rates, particularly for seborrheic keratosis (2.01%) and atypical nevi (3.24%).

Conclusions:

  • Automated artifact removal and border detection significantly improve lesion segmentation in dermoscopy.
  • The proposed method enhances sensitivity and specificity for diagnosing skin lesions.