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Unsupervised Image Segmentation Using THMRF Model

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Hybrid Intelligent Systems (HIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1375))

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Abstract

In this paper, a segmentation algorithm of brain magnetic resonance imaging is proposed. The proposed method rests on Tweedie hidden Markov random field processing and the Expectation- Method of moments and Maximization algorithm. The algorithm was validated on synthetic and real images. The basic merit of our proposed method of segmentation lies in its ability to quantify the complex structure as brain tumor.

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Author information

Authors and Affiliations

  1. Laboratory of Probability and Statistics, Sfax University, B.P. 1171, Sfax, Tunisia

    Zitouni Mouna & Masmoudi Afif

  2. Laboratoire d’Informatique Signal et Image de la Côte d’Opale (LISIC-EA 4491), ULCO, 50 Rue Ferdinand Buisson BP 719, 62228, Calais Cedex, France

    Zribi Mourad

Authors
  1. Zitouni Mouna
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  2. Zribi Mourad
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  3. Masmoudi Afif
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Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Solothurn, Switzerland

    Thomas Hanne

  3. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, AL, USA

    Niketa Gandhi

  5. Universidade Federal da Bahia, Salvador, Bahia, Brazil

    Tatiane Nogueira Rios

  6. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Mouna, Z., Mourad, Z., Afif, M. (2021). Unsupervised Image Segmentation Using THMRF Model. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_5

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