A Thinning Model for Handwriting-Like Image Skeleton

Zhu, Shi-jiao; Yang, Jun; Zhu, Xiaoyan

doi:10.1007/978-3-319-01766-2_79

Cited by 1 publication

(1 citation statement)

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“…Skeletonizing an image aims to provide a wired representation of objects while preserving the essential information carried by each object in the simplest form. At the beginning its application concerned the character recognition (Arcplli et al, 1985) then it was extended to other research area namely biometrics, road extraction, medical imaging for the detection of blood vessels, or extraction of bone architecture (Zhu et al, 2014;Beumier and Neyt, 2017). There is currently a wide variety of skeletonizing methods; the most common are based on thinning, on distance maps, or on hybrid techniques, but each of them gives a different result (Saha et al, 2016;De Oliveira et al, 2009):…”

Section: Skeletonizingmentioning

confidence: 99%

New and Efficient Features for Skin Lesion Classification based on Skeletonization

Filali¹,

Khoukhi²,

Sabri³

et al. 2019

Journal of Computer Science

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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.

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