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Macroscopic Pigmented Skin Lesion Segmentation and Its Influence on Lesion Classification and Diagnosis

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Color Medical Image Analysis

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 6))

Abstract

Melanoma is a type of malignant pigmented skin lesion, and currently is among the most dangerous existing cancers. However, differentiating malignant and benign cases is a hard task even for experienced specialists, and a computer-aided diagnosis system can be an useful tool. Usually, the system starts by pre-processing the image, i.e. removing undesired artifacts such as hair, freckles or shading effects. Next, the system performs a segmentation step to identify the lesion boundaries. Finally, based on the image area identified as lesion, several features are computed and a classification is provided. In this chapter we describe all these steps, giving special attention to segmentation approaches for pigmented skin lesions, proposed for standard camera images (i.e. simple color photographs). Next, we compare the segmentation results to identify which techniques have more accurate results, and discuss how these results may influence in the following steps: the feature extraction and the final lesion classification.

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Notes

  1. 1.

    The rim is dilated by 2 pixels, producing a 5 pixels wide region centered at the lesion rim, as suggested in [17].

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Authors and Affiliations

  1. Instituto de Informática, UFRGS, Av. Bento Gonçalves 9500, 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 Pablo G. Cavalcanti .

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Editors and Affiliations

  1. Computer Science Department, Louisiana State University Shreveport, One University Place, Shreveport, 71115, USA

    M. Emre Celebi

  2. , Department of Computer Science, Loughborough University, Epinal Way, Loughborough, LE11 3TU, United Kingdom

    Gerald Schaefer

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Cavalcanti, P.G., Scharcanski, J. (2013). Macroscopic Pigmented Skin Lesion Segmentation and Its Influence on Lesion Classification and Diagnosis. In: Celebi, M., Schaefer, G. (eds) Color Medical Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5389-1_2

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  • DOI: https://doi.org/10.1007/978-94-007-5389-1_2

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-5388-4

  • Online ISBN: 978-94-007-5389-1

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