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Evidence-Based of Improved Electron Tomogram Segmentation and Visualization Through High-Pass Domain Kernel in Bilateral Filter

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Advances in Visual Informatics (IVIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13051))

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Abstract

Segmentation is the most challenging task in image processing. To date, there is still no superior method can segment all types of images either in 2D or 3D. A popular technique: bilateral filter, a non-linear method used to efficiently eliminate noise while preserving object’s edges, has two parameters: domain and range kernel, and widely used for medical image segmentation. Unlike medical images, noise on electron tomogram, are diverse in terms of its density and intensity. Therefore, this research proposed a modification on the two kernels: the existing Gaussian domain kernel replaced by Sobel and Canny edge detector to improve the edge detector element and the existing Gaussian range kernel replaced by an inverted Gaussian kernel serves to highlight different regions. The results of bilateral filter using Canny as the high-pass domain kernel show efficient detection accordingly as the pixel intensity changes, in comparison to the results of Sobel as domain kernel, and to the techniques engaging filter along: Sobel and Canny edge detectors.

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Acknowledgments

The authors wish to thank Universiti Sains Malaysia for the support it has extended in the completion of the present research through Short Term Grant University Grant No: 304/PKOMP/6315321.

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Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, Main Campus, Penang, 11800, Gelugor, Malaysia

    Nur Intan Raihana Ruhaiyem & Noor Shariah Ismail

Authors
  1. Nur Intan Raihana Ruhaiyem
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  2. Noor Shariah Ismail
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Corresponding author

Correspondence to Nur Intan Raihana Ruhaiyem .

Editor information

Editors and Affiliations

  1. Universiti Tenaga Nasional, Selangor, Malaysia

    Halimah Badioze Zaman

  2. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  3. National Central University, Jhongli, Taiwan

    Timothy K. Shih

  4. Queen Mary University of London, London, UK

    Sergio Velastin

  5. Toyo University, Tokyo, Japan

    Tada Terutoshi

  6. University of Southern Denmark, Odense, Denmark

    Bo Nørregaard Jørgensen

  7. Universiti Tenaga Nasional, Selangor, Malaysia

    Hazleen Aris

  8. Universiti Tenaga Nasional, Selangor, Malaysia

    Nazrita Ibrahim

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Ruhaiyem, N.I.R., Ismail, N.S. (2021). Evidence-Based of Improved Electron Tomogram Segmentation and Visualization Through High-Pass Domain Kernel in Bilateral Filter. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2021. Lecture Notes in Computer Science(), vol 13051. Springer, Cham. https://doi.org/10.1007/978-3-030-90235-3_14

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  • DOI: https://doi.org/10.1007/978-3-030-90235-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90234-6

  • Online ISBN: 978-3-030-90235-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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