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

Victor Gonzalez-Castro, Johan Debayle*, Yanal Wazaefi, Mehdi Rahim, Caroline Gaudy-Marqueste, Jean Jacques Grob, Bernard Fertil

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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).
Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume9534
ISBN (Print)9781628416992
DOIs
Publication statusPublished - 2015
Event12th International Conference on Quality Control by Artificial Vision - Le Creusot, France
Duration: 3 Jun 20155 Jun 2015

Conference

Conference12th International Conference on Quality Control by Artificial Vision
Country/TerritoryFrance
CityLe Creusot
Period3/06/155/06/15

Keywords

  • Color adaptive neighborhoods
  • Color texture description
  • Mathematical morphology
  • Melanoma
  • Self-organizing maps

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