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A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions

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Color Medical Image Analysis

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

Abstract

This chapter proposes a novel hierarchical classification system based on the K-Nearest Neighbors (K-NN) model and its application to non-melanoma skin lesion classification. Color and texture features are extracted from skin lesion images. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is embedded in the hierarchical framework that chooses the most relevant feature subsets at each node of the hierarchy. The accuracy of the proposed hierarchical scheme is higher than 93 % in discriminating cancer and potential at risk lesions from benign lesions, and it reaches an overall classification accuracy of 74 % over five common classes of skin lesions, including two non-melanoma cancer types. This is the most extensive known result on non-melanoma skin cancer classification using color and texture information from images acquired by a standard camera (non-dermoscopy).

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Acknowledgement

We thank the Wellcome Trust for funding this project (Grant No: 083928/Z/07/Z).

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Authors and Affiliations

  1. School of Informatics, University of Edinburgh, Edinburgh, UK

    Lucia Ballerini & Robert B. Fisher

  2. Department of Dermatology, University of Edinburgh, Edinburgh, UK

    Ben Aldridge & Jonathan Rees

Authors
  1. Lucia Ballerini
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  2. Robert B. Fisher
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  3. Ben Aldridge
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  4. Jonathan Rees
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Corresponding author

Correspondence to Lucia Ballerini .

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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

Appendix

Appendix

List of texture features selected for each level of the final tree. (See Table 8.)

Table 8 Legend: R = Red, G = Green, B = Blue, H = Hue, S = Saturation, V = Value, L, a, b = Lab color space. Texture features are defined in [30]
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Ballerini, L., Fisher, R.B., Aldridge, B., Rees, J. (2013). A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions. 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_4

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  • DOI: https://doi.org/10.1007/978-94-007-5389-1_4

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  • Online ISBN: 978-94-007-5389-1

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