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Automatic Diagnosis of Melanoma Based on the 7-Point Checklist

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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

An image based system implementing a well-known diagnostic method is disclosed for the automatic detection of melanomas as support to clinicians. The software procedure is able to recognize automatically the skin lesion within the digital image, measure morphological and chromatic parameters, carry out a suitable classification for detecting the dermoscopic structures provided by the 7-Point Checklist. Advanced techniques are introduced at different stages of the image processing pipeline, including the border detection, the extraction of low-level features and scoring of high order features.

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Author information

Authors and Affiliations

  1. Dermatology Research, University Federico II of Naples, Via S. Pansini 5, 80131, Naples, Italy

    Gabriella Fabbrocini, Valerio De Vita, Sara Cacciapuoti & Giuseppe Di Leo

  2. Department of Industrial Engineering (DIIn), University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano, Sa, Italy

    Giuseppe Di Leo, Consolatina Liguori, Alfredo Paolillo, Antonio Pietrosanto & Paolo Sommella

Authors
  1. Gabriella Fabbrocini
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  2. Valerio De Vita
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  3. Sara Cacciapuoti
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  4. Giuseppe Di Leo
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  5. Consolatina Liguori
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  6. Alfredo Paolillo
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  7. Antonio Pietrosanto
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  8. Paolo Sommella
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Corresponding authors

Correspondence to Consolatina Liguori or Paolo Sommella .

Editor information

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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Fabbrocini, G. et al. (2014). Automatic Diagnosis of Melanoma Based on the 7-Point Checklist. 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_4

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  • DOI: https://doi.org/10.1007/978-3-642-39608-3_4

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  • Online ISBN: 978-3-642-39608-3

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