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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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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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Related Experiment Video

Updated: Feb 18, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
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Automatic psoriasis lesion segmentation in two-dimensional skin images using multiscale superpixel clustering.

Yasmeen George1, Mohammad Aldeen1, Rahil Garnavi1,2

  • 1University of Melbourne, Department of Electrical and Electronic Engineering, Victoria, Australia.

Journal of Medical Imaging (Bellingham, Wash.)
|November 21, 2017
PubMed
Summary

This study introduces an automated method for segmenting psoriasis lesions using multiscale superpixels and k-means clustering. The technique accurately identifies psoriasis skin areas, improving upon existing methods for PASI scoring.

Keywords:
K-means clusteringmedical image analysismultiscale superpixels segmentationpsoriasis severity scoring

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Area of Science:

  • Dermatology and Medical Imaging
  • Computer Vision and Image Analysis

Background:

  • Psoriasis is a chronic skin condition requiring visual assessment.
  • The Psoriasis Area and Severity Index (PASI) is the standard for measuring lesion severity.
  • Accurate psoriasis lesion segmentation is crucial for objective PASI scoring.

Purpose of the Study:

  • To develop an automatic method for psoriasis skin lesion segmentation.
  • To enhance the accuracy and efficiency of PASI scoring through improved lesion identification.

Main Methods:

  • Utilized multiscale superpixel segmentation on the CIE-L*a*b* color space.
  • Applied k-means clustering to differentiate between normal and lesioned skin areas.
  • Fused results from multiple scales using majority voting for final segmentation.

Main Results:

  • Achieved high performance on 457 psoriasis images, demonstrating effectiveness on hairy skin and diverse lesions.
  • The CIE-L*a*b* color space proved superior for psoriasis lesion analysis.
  • Obtained Dice coefficient of 0.783%, Jaccard index of 0.698%, and pixel accuracy of 86.99%.

Conclusions:

  • The proposed multiscale superpixel and k-means clustering method offers a highly effective and efficient approach to psoriasis lesion segmentation.
  • This automated method significantly outperforms existing techniques, showing at least a 20% accuracy improvement.
  • The findings support the use of this method for objective and reliable PASI scoring in clinical practice.