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Using Local Normalization and Local Thresholding in the Detection of Small Objects in MR Brain Images

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The Latest Developments and Challenges in Biomedical Engineering (PCBEE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 746))

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Abstract

A lot of time and effort has been put into finding an automatic segmentation system which could detect the region and position of white matter hypersensitivities in MRI brain scans. At the cellular level, changes in the white matter of the brain can be understood as a loss of myelin around axons. These changes might be detected by MRI due to local changes in water content. By establishing a method which could detect such irregularities, it would be possible to help diagnose some serious autoimmune diseases. In this work, a wide variety of local thresholding algorithms that are applied after local normalization is evaluated in terms of how accurately they segment these hypersensitivities. With the use of ImageJ, the following algorithms were tested: Bernsen, Contrast, Mean, Median, MidGrey, Otsu, Sauvola and SDA. The most accurate segmentation results are presented for all algorithms. For most of the ALT algorithms, the use of local normalization increased the achieved Dice value by as much as 100%. Contrary to what might be expected, the best results were achieved for various values of the sigma1 and sigma2 parameters of local normalization, both for single images and for a group of algorithms for a given image, thus it is difficult to apply the presented solutions to other examples.

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Acknowledgements

This publication was funded by AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, KBIB no 16.16.120.773.

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Authors and Affiliations

  1. Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza 30 Av., 30–059, Krakow, Poland

    Patrycja Kwiek & Elżbieta Pociask

Authors
  1. Patrycja Kwiek
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  2. Elżbieta Pociask
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Correspondence to Elżbieta Pociask .

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Editors and Affiliations

  1. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Paweł Strumiłło

  2. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Artur Klepaczko

  3. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Michał Strzelecki

  4. Institute of Materials Science, Lodz University of Technology, Lodz, Poland

    Dorota Bociąga

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Kwiek, P., Pociask, E. (2024). Using Local Normalization and Local Thresholding in the Detection of Small Objects in MR Brain Images. In: Strumiłło, P., Klepaczko, A., Strzelecki, M., Bociąga, D. (eds) The Latest Developments and Challenges in Biomedical Engineering. PCBEE 2023. Lecture Notes in Networks and Systems, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-031-38430-1_5

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  • DOI: https://doi.org/10.1007/978-3-031-38430-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-38429-5

  • Online ISBN: 978-3-031-38430-1

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