Automatic lesion border selection in dermoscopy images using morphology and color features. Issue 4 (14th March 2019) - Mishra, Nabin K.; Kaur, Ravneet; Kasmi, Reda; Hagerty, Jason R.; LeAnder, Robert; Stanley, Ronald J.; Moss, Randy H.; Stoecker, William V.
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.