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Melanoma Decision Support Using Lighting-Corrected Intuitive Feature Models

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Book cover Computer Vision Techniques for the Diagnosis of Skin Cancer

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

Abstract

Skin lesion analysis using standard camera images has received limited attention from the scientific community due to its technical complexity and scarcity of data. The images are privy to lighting variations caused by uneven source lighting, and unconstrained differences in resolution, scale, and equipment. In this chapter, we propose a framework that performs illumination correction and feature extraction on photographs of skin lesions acquired using standard consumer-grade cameras. We apply a multi-stage illumination correction algorithm and define a set of high-level intuitive features (HLIF) that quantifies the level of asymmetry and border irregularity about a lesion. This lighting-corrected intuitive feature model framework can be used to classify skin lesion diagnoses with high accuracy. The framework accurately corrects the illumination variations and achieves high and precise sensitivity (95 % confidence interval (CI), 73.1–73.5 %) and specificity (95 % CI, 72.0–72.4 %) using a linear support vector machine classifier with cross-validation trials. It exhibits higher test-retest reliability than the much larger state-of-the-art low-level feature set (95 % CI, 78.1–79.7 % sensitivity, 75.3–76.3 % specificity). Combining our framework with these low-level features attains sensitivity (95 % CI, 83.3–84.8 %) and specificity (95 % CI, 79.7–80.1 %), which is more accurate and reliable than classification using the low-level feature set.

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Acknowledgments

This research was sponsored by Agfa Healthcare Inc., Ontario Centres of Excellence (OCE), and the Natural Sciences and Engineering Research Council (NSERC) of Canada.

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Authors and Affiliations

  1. Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada

    Robert Amelard, Jeffrey Glaister, Alexander Wong & David A. Clausi

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  1. Robert Amelard
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  4. David A. Clausi
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Correspondence to Robert Amelard .

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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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Amelard, R., Glaister, J., Wong, A., Clausi, D.A. (2014). Melanoma Decision Support Using Lighting-Corrected Intuitive Feature Models. 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_7

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  • DOI: https://doi.org/10.1007/978-3-642-39608-3_7

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