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Skin Res Technol. 2019 Mar 14. doi: 10.1111/srt.12685. [Epub ahead of print]

Automatic lesion border selection in dermoscopy images using morphology and color features.

Author information

1
Stoecker and Associates, Rolla, Missouri.
2
Department of Electrical and Computer Engineering, Southern Illinois University Edwardsville, Edwardsville, Illinois.
3
Department of Electrical Engineering, University of Bejaia, Bejaia, Algeria.
4
Department of Electrical Engineering, University of Bouira, Bouira, Algeria.
5
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri.

Abstract

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.

KEYWORDS:

border; classifier; dermoscopy; image analysis; lesion segmentation; melanoma; skin cancer

PMID:
30868667
DOI:
10.1111/srt.12685

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