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Texture Information in Melanocytic Skin Lesion Analysis Based on Standard Camera Images

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Computer Vision Techniques for the Diagnosis of Skin Cancer

Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

The classification of melanocytic skin lesions is a very difficult task, and usually computer-aided diagnosis systems or screening systems focus on reproducing medical criteria as the ABCD rule. However, the texture information can also contribute significantly for the lesion classification, since malignant cases tends to present texture patterns different from benign cases. In this chapter, we detail five representative sets of features that have been proposed in the literature for the representation of melanocytic lesions texture information, and then we analyze how these features distinguish between malignant and benign classes using two well known classifiers.

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Notes

  1. 1.

    The results are presented in a line chart, and the exact number is not provided.

  2. 2.

    The higher histogram peaks are associated to healthy skin regions in Figs. 4c and d.

  3. 3.

    Cavalcanti and Scharcanski [6] suggest using \(\sigma = 1,\frac{11}{7}, \frac{15}{7}, ..., \frac{43}{7}\), and filter window sizes of \(7\sigma \times 7\sigma \).

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Authors and Affiliations

  1. Instituto de Informática, UFRGS, Porto Alegre, Brazil

    Pablo G. Cavalcanti & Jacob Scharcanski

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  1. Pablo G. Cavalcanti
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  2. Jacob Scharcanski
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Correspondence to Jacob Scharcanski .

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Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. Computer Science dept., Louisiana State University Shreveport, Shreveport, Louisiana, USA

    M. Emre Celebi

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Cavalcanti, P.G., Scharcanski, J. (2014). Texture Information in Melanocytic Skin Lesion Analysis Based on Standard Camera Images. In: Scharcanski, J., Celebi, M. (eds) Computer Vision Techniques for the Diagnosis of Skin Cancer. Series in BioEngineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39608-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-39608-3_8

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