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A Texture Analysis Approach for Spine Metastasis Classification in T1 and T2 MRI

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Bioinformatics and Biomedical Engineering (IWBBIO 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10814))

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Abstract

This paper presents a learning based approach for the classification of pathological vertebrae. The proposed method is applied to spine metastasis, a malignant tumor that develops inside bones and requires a rapid diagnosis for an effective treatment monitoring. We used multiple texture analysis techniques to extract useful features from two co-registered MR images sequences (T1, T2). These MRIs are part of a diagnostic protocol for vertebral metastases follow up. We adopted a slice by slice MRI analysis of 153 vertebra region of interest. Our method achieved a classification accuracy of \(90.17\% \pm 5.49\), using only a subset of 67 relevant selected features from the initial 142.

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Notes

  1. 1.

    Multidisciplinary and autonomous hospital totally dedicated to cancer located in Brussels, Belgium.

  2. 2.

    www.slicer.org.

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Author information

Authors and Affiliations

  1. Computer Science Department, Faculty of Engineering, University of Mons, Mons, Belgium

    Mohamed Amine Larhmam, Saïd Mahmoudi & Mohammed Benjelloun

  2. Radiology Department, Jules Bordet Institute, Université Libre de Bruxelles, Brussels, Belgium

    Stylianos Drisis

Authors
  1. Mohamed Amine Larhmam
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  2. Saïd Mahmoudi
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  3. Stylianos Drisis
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  4. Mohammed Benjelloun
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Corresponding author

Correspondence to Mohamed Amine Larhmam .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Francisco Ortuño

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Larhmam, M.A., Mahmoudi, S., Drisis, S., Benjelloun, M. (2018). A Texture Analysis Approach for Spine Metastasis Classification in T1 and T2 MRI. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10814. Springer, Cham. https://doi.org/10.1007/978-3-319-78759-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-78759-6_19

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  • Online ISBN: 978-3-319-78759-6

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