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Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus
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Three-phase general border detection method for dermoscopy images using non-uniform illumination correction.

Kerri-Ann Norton1, Hitoshi Iyatomi, M Emre Celebi

  • 1BioMaPS Institute, Rutgers University, Piscataway, NJ, USA. knorton4@jhmi.edu

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)
|November 19, 2011
PubMed
Summary

A new automatic segmentation program accurately detects skin lesion borders in dermoscopy images. This method excels at segmenting both non-melanocytic skin lesions (NoMSLs) and melanocytic skin lesions (MSLs), improving diagnostic capabilities.

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Accurate skin lesion segmentation is crucial for computer-aided diagnosis.
  • Existing methods primarily focus on melanocytic skin lesions (MSLs), with limited success for diverse non-melanocytic skin lesions (NoMSLs).
  • Segmentation of NoMSLs is challenging due to their wide variety.

Purpose of the Study:

  • To develop an automatic segmentation program for detecting skin lesion borders in dermoscopy images.
  • To address the challenge of segmenting both MSLs and NoMSLs.
  • To provide a quantitative and objective tool for skin lesion classification.

Main Methods:

  • Developed an automatic segmentation program incorporating pre-processing, general lesion segmentation with illumination correction, and bright region segmentation.
  • The method was tested on 107 NoMSLs and 319 MSLs.
  • Performance was evaluated against manual segmentations by multiple dermatologists.

Main Results:

  • Achieved high precision and recall scores: 84.5%/88.5% for NoMSLs and 93.9%/93.8% for MSLs.
  • The method's accuracy was competitive with or superior to five recently published methods.
  • Demonstrated robust performance across different lesion types.

Conclusions:

  • The developed method is the first to successfully detect borders of both non-melanocytic and melanocytic skin lesions.
  • This advancement offers a significant improvement in automated skin lesion segmentation.
  • The program provides a valuable tool for enhancing computer-aided diagnosis in dermatology.