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Melanoma Detection Using Spatial and Spectral Analysis on Superpixel Graphs

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Abstract

Melanoma is the most fatal type of skin cancer. Detection of melanoma from dermoscopic images in an early stage is critical for improving survival rates. Numerous image processing methods have been devised to discriminate between melanoma and benign skin lesions. Previous studies show that the detection performance depends significantly on the skin lesion image representations and features. In this work, we propose a melanoma detection approach that combines graph-theoretic representations with conventional dermoscopic image features to enhance the detection performance. Instead of using individual pixels of skin lesion images as nodes for complex graph representations, superpixels are generated from the skin lesion images and are then used as graph nodes in a superpixel graph. An edge of such a graph connects two adjacent superpixels where the edge weight is a function of the distance between feature descriptors of these superpixels. A graph signal can be defined by assigning to each graph node the output of some single-valued function of the associated superpixel descriptor. Features are extracted from weighted and unweighted graph models in the vertex domain at both local and global scales and in the spectral domain using the graph Fourier transform (GFT). Other features based on color, geometry and texture are extracted from the skin lesion images. Several conventional and ensemble classifiers have been trained and tested on different combinations from those features using two datasets of dermoscopic images from the International Skin Imaging Collaboration (ISIC) archive. The proposed system achieved an AUC of \(99.91\%\), an accuracy of \(97.40\%\), a specificity of \(95.16\%\) and a sensitivity of \(100\%\).

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Cairo University, Giza, Egypt

    Mahmoud H. Annaby

  2. Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt

    Asmaa M. Elwer, Muhammad A. Rushdi & Mohamed E. M. Rasmy

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  1. Mahmoud H. Annaby
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  2. Asmaa M. Elwer
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Correspondence to Muhammad A. Rushdi.

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All authors have no conflict of interest and contributed equally in this work for conceptualization, methodology, formal analysis, investigation, and writing.

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The datasets used in this work (ISBI 2016 and ISBI 2017) are publically available.

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Annaby, M.H., Elwer, A.M., Rushdi, M.A. et al. Melanoma Detection Using Spatial and Spectral Analysis on Superpixel Graphs. J Digit Imaging 34, 162–181 (2021). https://doi.org/10.1007/s10278-020-00401-6

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