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.
N. K. Mishra et al., "Automatic Lesion Border Selection in Dermoscopy Images using Morphology and Color Features," Skin Research and Technology, vol. 25, no. 4, pp. 544-552, John Wiley & Sons, Jul 2019.
The definitive version is available at https://doi.org/10.1111/srt.12685
Electrical and Computer Engineering
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)
Article - Journal
© 2019 John Wiley & Sons, All rights reserved.
01 Jul 2019