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Brain Blood Vessel Segmentation in Hyperspectral Images Through Linear Operators

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 13879))

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Abstract

Tissue classification tasks that rely on multidimensional data, such as spectral information, sometimes face issues related to the nature of their own characteristics when different biological components share similar spectrum. In a situation of in-vivo brain tumor location during a surgical operation, especially when applying machine learning techniques, relying solely on the spectral information of each sample may not be enough to provide a correct identification of all the tissues involved. In order to overcome this problem, in this work we propose to reduce conflicting classification pixels, i.e. vascular versus tumor tissues. To do so, morphological operators can supply support to a pixel-wise classification by exploiting the spatial characteristics present in vascular tissue. Hence, we have evaluated the suitability of linear operators for brain vessels segmentation in a context of hyperspectral video classification. The parameters of the operator along with the selection of the most suitable spectral band to process were chosen via optimization of the amount of vascular pixels detected and error metrics. The segmentation algorithm was implemented for both CPU and GPU platforms achieving a performance compatible with real-time classification purposes on the last one. Objective results show an average segmentation of the 68% of the vein and arteries present in the ground truth with less than a 10% of error selecting pixels from other tissues of interest such as healthy brain and tumor.

This work was supported by the Spanish Government through TALENT-HIPSTER project (PID2020-116417RB-C41).

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Author information

Authors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Guillermo Vazquez, Manuel Villa, Alberto Martín-Pérez, Jaime Sancho, Gonzalo Rosa, Pedro L. Cebrián, Pallab Sutradhar, Alejandro Martinez de Ternero, Miguel Chavarrías, Eduardo Juarez & César Sanz

  2. Instituto de Investigación Sanitaria Hospital 12 de Octubre, Madrid, Spain

    Alfonso Lagares

Authors
  1. Guillermo Vazquez

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  2. Manuel Villa

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  3. Alberto Martín-Pérez

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  4. Jaime Sancho

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  5. Gonzalo Rosa

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  6. Pedro L. Cebrián

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  7. Pallab Sutradhar

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  8. Alejandro Martinez de Ternero

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  9. Miguel Chavarrías

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  10. Alfonso Lagares

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  11. Eduardo Juarez

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  12. César Sanz

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Corresponding author

Correspondence toMiguel Chavarrías.

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Editors and Affiliations

  1. Polytechnic University of Madrid, Madrid, Spain

    Miguel Chavarrías

  2. Polytechnic University of Madrid, Madrid, Spain

    Alfonso Rodríguez

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Vazquez, G.et al. (2023). Brain Blood Vessel Segmentation in Hyperspectral Images Through Linear Operators. In: Chavarrías, M., Rodríguez, A. (eds) Design and Architecture for Signal and Image Processing. DASIP 2023. Lecture Notes in Computer Science, vol 13879. Springer, Cham. https://doi.org/10.1007/978-3-031-29970-4_3

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