Automatic Lesion Border Selection in Dermoscopy Images using Morphology and Color Features


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.


Electrical and Computer Engineering

Second Department



This publication was made possible by SBIR Grants R43 CA153927-01 and R44 CA101639-02A2 of the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Keywords and Phrases

Classification (of information); Classifiers; Computer aided diagnosis; Decision trees; Dermatology; Image analysis; Oncology; Border; Dermoscopy; Lesion segmentations; Melanoma; Skin cancers; Image segmentation

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


File Type





© 2019 John Wiley & Sons, All rights reserved.

Publication Date

01 Jul 2019