Research Article Open Access

New and Efficient Features for Skin Lesion Classification based on Skeletonization

Youssef Filali1, Hasnae El Khoukhi1, My Abdelouahed Sabri1, Ali Yahyaouy1 and Abdellah Aarab1
  • 1 USMBA, Morocco

Abstract

This paper presents a new approach to detect and classify skin lesions for melanoma diagnosis with high accuracy. Skin lesion detection is based on an image decomposition into two components using the Partial Differential Equation (PDE). The first component that sufficiently preserves the contour is thus exploited to have an adequate segmentation of image lesion while the second component provides a good characterization of the texture. Moreover, to improve the classification accuracy, new and powerful features extracted by skeletonization of the lesion are presented. These features are compared and combined with well-known features from the literature. Features engineering was applied to select the most relevant features to be retained for the classification phase. The proposed approach was implemented and tested on a large database and gave a good classification accuracy compared to recent approaches from the literature.

Journal of Computer Science
Volume 15 No. 9, 2019, 1225-1236

DOI: https://doi.org/10.3844/jcssp.2019.1225.1236

Submitted On: 28 January 2019 Published On: 2 September 2019

How to Cite: Filali, Y., El Khoukhi, H., Sabri, M. A., Yahyaouy, A. & Aarab, A. (2019). New and Efficient Features for Skin Lesion Classification based on Skeletonization. Journal of Computer Science, 15(9), 1225-1236. https://doi.org/10.3844/jcssp.2019.1225.1236

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Keywords

  • Image Processing
  • Skin Cancer
  • PDE Multiscale Decomposition
  • Texture
  • Shape and Color Analysis
  • Features Engineering
  • Skeletonization