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Conference Papers Year : 2015

Automatic classification of skin lesions using color mathematical morphology-based texture descriptors

Abstract

In this paper an automatic classification method of skin lesions from dermoscopic images is proposed. This method is based on color texture analysis based both on color mathematical morphology and Kohonen Self-Organizing Maps (SOM), and it does not need any previous segmentation process. More concretely, mathematical morphology is used to compute a local descriptor for each pixel of the image, while the SOM is used to cluster them and, thus, create the texture descriptor of the global image. Two approaches are proposed, depending on whether the pixel descriptor is computed using classical (i.e. spatially invariant) or adaptive (i.e. spatially variant) mathematical morphology by means of the Color Adaptive Neighborhoods (CANs) framework. Both approaches obtained similar areas under the ROC curve (AUC): 0.854 and 0.859 outperforming the AUC built upon dermatologists' predictions (0.792).
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Dates and versions

emse-01163688 , version 1 (15-06-2015)

Identifiers

  • HAL Id : emse-01163688 , version 1

Cite

Victor Gonzalez-Castro, Johan Debayle, Yanal Wazaefi, Mehdi Rahim, Caroline Gaudy-Marqueste, et al.. Automatic classification of skin lesions using color mathematical morphology-based texture descriptors. Twelfth International Conference on Quality Control by Artificial Vision, Le2i - Laboratoire Electronique, Informatique et Image, Jun 2015, Le Creusot, France. pp.[9534-3] ; doi:10.1117/12.2182592. ⟨emse-01163688⟩
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