Related Experiment Video For border
Updated: Jan 27, 2026
Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus
Published on: May 16, 2025
Automatic lesion border selection in dermoscopy images using morphology and color features
Nabin K Mishra1, Ravneet Kaur2, Reda Kasmi3,4
1Stoecker and Associates, Rolla, Missouri.
Purpose:
We present a classifier for automatically selecting a lesion border for dermoscopy skin lesion images, to aid in computer-aided diagnosis of melanoma. Variation in photographic technique of dermoscopy images makes segmentation of skin lesions a difficult problem. No single algorithm provides an acceptable lesion border to allow further processing of skin lesions.
Methods:
We present a random forests border classifier model to select a lesion border from 12 segmentation algorithm borders, graded on a "good-enough" border basis. Morphology and color features inside and outside the automatic border are used to build the model.
Results:
For a random forests classifier applied to an 802-lesion test set, the model predicts a satisfactory border in 96.38% of cases, in comparison to the best single border algorithm, which detects a satisfactory border in 85.91% of cases.
Conclusion:
The performance of the classifier-based automatic skin lesion finder is found to be better than any single algorithm used in this research.
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