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Texture Analysis in Skin Cancer Tumor Imaging

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Multimodal Optical Diagnostics of Cancer

Abstract

The application of different statistical, frequency, and stochastic methods of texture image analysis for differentiation of various malignant and benign tumors is discussed. The skin surface dermatoscopic images were analyzed using Haar transform, Local Binary Patterns, and color histograms evolution. It was found that classification results may be significantly increased by calculating comparative textural descriptors including personal properties of the healthy skin. Optical Coherence Tomography (OCT) technique was used for detecting of internal inhomogeneities. Forty-four optical and textural features extracted from OCT images of healthy and diseased skin have been analyzed using a linear Support Vector Machine classification with k-fold cross-validation and 5-layer decision trees. It was demonstrated that Precision and Recall exceed 97% in a multi-class (Melanoma, Basal Cell Carcinoma, Nevus, etc.) recognition procedure due to an implementation of multi-texture analysis when each from used texture features (Haralick, Tamura, Gabor, Markov Random Field, Complex Directional Field, fractal dimensions) complements each other. It makes possible the recognition of various tumors (malignant as well as benign) contemporaneously with a high-score identification of a tumor type in real clinical conditions.

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Acknowledgments

This research was supported by the grant # 19-52-18001 Bolg_a of the Russian Foundation of Basic Research. We are very thankful to Dr. Wei Gao from Ningbo University of Technology, China for Matlab scripts for denoising and fractal dimension calculating, as well as not a long but productive work together in Samara National Research University.

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Authors and Affiliations

  1. Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russian Federation

    Oleg O. Myakinin, Dmitry S. Raupov & Semyon G. Konovalov

  2. Department of Technical Cybernetics, Samara National Research University, Samara, Russian Federation

    Alexander G. Khramov

  3. Image Processing Systems Institute of RAS - Branch of the FSRC “Crystallography and Photonics” RAS, Samara, Russian Federation

    Alexander G. Khramov

  4. Department of Oncology, Samara State Medical University, Samara, Russian Federation

    Sergey V. Kozlov & Alexander A. Moryatov

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  1. Oleg O. Myakinin
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  4. Semyon G. Konovalov
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Correspondence to Oleg O. Myakinin .

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  1. Department of Optics and Biophotonics, Saratov State University, Saratov, Russia

    Valery V. Tuchin

  2. Leibniz-Institute of Photonic Technology e.V. and Institute of Physical Chemistry and Abbe Center of Photonics, University of Jena, Jena, Thüringen, Germany

    Jürgen Popp

  3. Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia

    Valery Zakharov

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Myakinin, O.O., Khramov, A.G., Raupov, D.S., Konovalov, S.G., Kozlov, S.V., Moryatov, A.A. (2020). Texture Analysis in Skin Cancer Tumor Imaging. In: Tuchin, V.V., Popp, J., Zakharov, V. (eds) Multimodal Optical Diagnostics of Cancer. Springer, Cham. https://doi.org/10.1007/978-3-030-44594-2_13

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