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Segmenting MR Images Through Texture Extraction and Multiplicative Components Optimization

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Scale Space and Variational Methods in Computer Vision (SSVM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14009))

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Abstract

The segmentation of MRI is a challenging task due to artifacts introduced by the acquisition process, like bias field and noise. In this paper, using a cartoon-texture decomposition of the image, we present a strategy that segments the cartoon processed by simultaneous bias correction and denoising. Preliminary numerical tests show that our method is effective in segmenting MRI data corrupted by noise.

This work was partially supported by Istituto Nazionale di Alta Matematica - Gruppo Nazionale per il Calcolo Scientifico (INdAM-GNCS, ICAR-CNR INdAM Research Unit) and by the Italian Ministry of University and Research under grant no. PON03PE_00060_5.

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Notes

  1. 1.

    https://github.com/spougkakiotis/IP_PMM.

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Acknowledgments

L. Antonelli would like to thank Simona Sada (ICAR-CNR) for her technical support.

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Authors and Affiliations

  1. Consiglio Nazionale delle Ricerche, Napoli, Italy

    Laura Antonelli

  2. Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy

    Valentina De Simone

  3. University College Dublin, Dublin, Ireland

    Marco Viola

Authors
  1. Laura Antonelli
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  2. Valentina De Simone
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  3. Marco Viola
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Corresponding author

Correspondence to Laura Antonelli .

Editor information

Editors and Affiliations

  1. CNRS, Université Côte d'Azur, Sophia-Antipolis, France

    Luca Calatroni

  2. University of Insubria, Como, Italy

    Marco Donatelli

  3. University of Bologna, Bologna, Italy

    Serena Morigi

  4. University of Modena and Reggio Emilia, Modena, Italy

    Marco Prato

  5. University of Genova, Genova, Italy

    Matteo Santacesaria

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Antonelli, L., De Simone, V., Viola, M. (2023). Segmenting MR Images Through Texture Extraction and Multiplicative Components Optimization. In: Calatroni, L., Donatelli, M., Morigi, S., Prato, M., Santacesaria, M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Springer, Cham. https://doi.org/10.1007/978-3-031-31975-4_39

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  • DOI: https://doi.org/10.1007/978-3-031-31975-4_39

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