Nabin K Mishra1, Ravneet Kaur2, Reda Kasmi3,4, Jason R Hagerty1, Robert LeAnder2, Ronald J Stanley5, Randy H Moss5, William V Stoecker1. 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.
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.
Authors: Ralph P Braun; Harold S Rabinovitz; Margeret Oliviero; Alfred W Kopf; Jean H Saurat Journal: Clin Dermatol Date: 2002 May-Jun Impact factor: 3.541