New and Efficient Features for Skin Lesion Classification based on Skeletonization

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.