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Cerebral Cortex Extraction Methods Based on a Priori Knowledge for T1-Weighted MRI Images

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Advances in Computational Collective Intelligence (ICCCI 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2166))

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Abstract

Cerebral Cortex Extraction (CCE) plays a significant role in clinical applications such as pre-surgical planning and tumor segmentation. However, designing an efficient CCE technique is still a challenging task. In this work, we propose two efficient methods for CCE from T1-weighted MRI images. The first method (named CCE-AK) is divided in two phases: Pretreatment phase and CCE phase. Indeed, the input image is firstly filtered by a Gaussian filter to smooth the image and reduce noise. Thereafter, we apply the anisotropic diffusion to improve the texture quality on the filtered image. Thus, a binary image is obtained after the integration of a priori knowledge and the thresholding steep using the Otsu's method to simplify treatment and eliminate non-brain portions. After that, we start the second phase by eroding the image via a structuring element to eliminate the outer brain parts. In order to extract the Cerebral Cortex (CC), we look for the Largest Connected Component (LCC) in the eroded image. Finally, we use the dilation operation to preserve the totality of the CC region. However, the LCC concept failed in few slices to identify the CC correctly. To address this issue, we introduce a second method (CCE2), which makes use of information in the adjacent slices. To assess the performance, experiments are conducted on different MRI datasets collected from the Surgical Planning Laboratory (SPL). The proposed methods achieve better results in both visual effects and objective criteria than three popular methods (SPM, BET and BSE).

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Authors and Affiliations

  1. LR16ES06 Laboratoire de Recherche en Informatique, Modélisation et Traitement de l’Information et de La Connaissance (LIMTIC), Université de Tunis el Manar, Institut Supérieur d’Informatique el Manar, Research Team SIIVA, 2 Rue Abou Rayhane Bayrouni, 2080, Ariana, Tunisia

    Hajer Ouerghi, Olfa Mourali & Ezzeddine Zagrouba

Authors
  1. Hajer Ouerghi
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  2. Olfa Mourali
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  3. Ezzeddine Zagrouba
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Corresponding author

Correspondence to Hajer Ouerghi .

Editor information

Editors and Affiliations

  1. and Technology, Wrocław University of Science, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. University of Leipzig, Leipzig, Germany

    Bogdan Franczyk

  3. University of Leipzig, Leipzig, Sachsen, Germany

    André Ludwig

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Nunez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. University of Münster, Münster, Germany

    Gottfried Vossen

  7. and Technology, Wrocław University of Science, Wrocław, Poland

    Adrianna Kozierkiewicz

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Ouerghi, H., Mourali, O., Zagrouba, E. (2024). Cerebral Cortex Extraction Methods Based on a Priori Knowledge for T1-Weighted MRI Images. In: Nguyen, NT., et al. Advances in Computational Collective Intelligence. ICCCI 2024. Communications in Computer and Information Science, vol 2166. Springer, Cham. https://doi.org/10.1007/978-3-031-70259-4_32

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  • DOI: https://doi.org/10.1007/978-3-031-70259-4_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-70258-7

  • Online ISBN: 978-3-031-70259-4

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