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Symmetry of Hue Distribution in the Images

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10842))

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Abstract

In the paper, a new symmetry measure is proposed to evaluate the symmetry/asymmetry of the hue distribution within the segmented part of the image. A new symmetry/asymmetry area measure (ASM) as well as their parts: the asymmetry measures of: the shape distribution (ASMShape), hue distribution (ASMHue) and structures distribution (ASMStuct) are proposed and discussed. In the paper, a dermatological asymmetry measure in shape (DASMShape) and hue (DASMHue) are presented and discussed thoroughly as well as their ASMShape and ASMHue applications. The hue distribution of the symmetry/asymmetry of the segmented skin lesion is discussed. One of the DASMHue measures is thoroughly presented. The results of the DASMHue algorithm based on the threshold binary masks using PH2 dataset shows stronger overestimating results but the total ratio 95.8% of correctly and overestimated cases is better than the ratio which takes into account only shape alone.

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Authors and Affiliations

  1. Faculty of Physics and Applied Informatics, University of Lodz, Pomorska street 149/153, 90-236, Lodz, Poland

    Piotr Milczarski

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  1. Piotr Milczarski
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Correspondence to Piotr Milczarski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Milczarski, P. (2018). Symmetry of Hue Distribution in the Images. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-91262-2_5

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