Automatic lesion border selection in dermoscopy images using morphology and color features.
Authors of this article are:
Mishra NK, Kaur R, Kasmi R, Hagerty JR, LeAnder R, Stanley RJ, Moss RH, Stoecker WV.
A summary of the article is shown below:
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.© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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This article is a good source of information and a good way to become familiar with topics such as: border;classifier;dermoscopy;image analysis;lesion segmentation;melanoma;skin cancer.