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A New Algorithm for Skin Lesion Border Detection in Dermoscopy Images.

E Meskini1, M S Helfroush1, K Kazemi1

  • 1Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.

Journal of Biomedical Physics & Engineering
|May 8, 2018
PubMed
Summary

This study introduces an automated method for segmenting skin lesions in dermoscopy images, effectively removing artifacts like hair and shading for improved skin cancer diagnosis. The novel approach achieves high accuracy, aiding in precise lesion border detection.

Keywords:
Active ContourMelanomaSegmentationDermoscopy

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Digital dermoscopy is crucial for skin lesion analysis.
  • Automated segmentation of dermoscopy images is vital for skin cancer diagnosis.
  • Artifacts like hair and shading in dermoscopy images necessitate robust detection methods.

Purpose of the Study:

  • To develop an automated method for segmenting skin lesions in dermoscopy images.
  • To address challenges posed by artifacts such as hair and shading.
  • To enhance the accuracy of lesion border detection for improved diagnostic capabilities.

Main Methods:

  • A novel segmentation method based on active contour is proposed.
  • Artifacts including hair pixels and shading are addressed through specific restoration and detection schemes.
  • Particle Swarm Optimization (PSO) optimizes RGB to grayscale conversion coefficients.
  • Multi-Otsu method generates an initial contour for active contour-based border detection.
  • Chan and Vese active contour model is utilized for final lesion segmentation.

Main Results:

  • The method was evaluated on 145 dermoscopic images (79 benign, 75 melanoma).
  • Achieved a mean accuracy of 94%.
  • Demonstrated high sensitivity (78.5%) and specificity (99%) in lesion detection.

Conclusions:

  • The proposed method accurately segments skin lesions from dermoscopy images.
  • The approach effectively handles artifacts, improving segmentation reliability.
  • This technique shows promise for enhancing automated skin cancer diagnosis.