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Updated: May 12, 2026

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus
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An effective hair removal algorithm for dermoscopy images.

Mohammad Taghi Bahreyni Toossi1, Hamid Reza Pourreza, Hoda Zare

  • 1Medical Physics Research Center, Medical Physics Department, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

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)
|April 9, 2013
PubMed
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This study introduces an automated algorithm for hair removal in dermoscopy images, significantly improving skin lesion segmentation accuracy. The method effectively detects and repairs hair pixels, aiding in more precise diagnostic analysis.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Vision

Background:

  • Dermoscopy is crucial for diagnosing pigmented skin lesions.
  • Human interpretation of dermoscopy images can be subjective and difficult.
  • Accurate skin lesion segmentation is hindered by hair artifacts.

Purpose of the Study:

  • To develop an automated algorithm for hair detection and removal in dermoscopy images.
  • To improve the precision of skin lesion segmentation and analysis.
  • To address the challenge of hair artifacts in dermatological imaging.

Main Methods:

  • A two-step algorithm involving adaptive Canny edge detection and morphological operators for hair segmentation.
  • Hair repair using multi-resolution coherence transport inpainting.
Keywords:
dermoscopy imageshair detectionhair removalmelanomaskin lesions

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  • Application to 50 dermoscopy images.

    • Main Results:

    • Quantitative analysis using True Detection Rate (TDR), False Positive Rate (FPR), and Detection Accuracy (DA) metrics.
    • Evaluation of hair repair using entropy, standard deviation, and co-occurrence matrix.
    • Demonstrated high accuracy in detecting and repairing hair pixels with minimal errors.

    Conclusions:

    • The proposed algorithm accurately detects and removes hair from dermoscopy images.
    • The hair removal process significantly enhances the segmentation accuracy of skin lesions.
    • This automated approach aids in more reliable dermatological image analysis.