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International Symposium on Artificial Intelligence and Signal Processing

AISP 2013: Artificial Intelligence and Signal Processing pp 111–120Cite as

Needle Detection in 3D Ultrasound Images Using Anisotropic Diffusion and Robust Fitting

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 427))

Abstract

Needle insertion is a minimally invasive medical procedure with a vast domain of applications. 3D localization of the needle is important in needle visual tracking by a physician and robotic automatic needle guidance. This paper investigates detection of the needle position in 3D ultrasound images. Ultrasound is a fast and non-invasive medical imaging modality that is suitable for intra-operative imaging. But unfortunately, ultrasound images suffer from speckle noise and other artifacts that degrade the image quality. We combined the RANSAC robust fitting algorithm with a structure adopted denoising method called anisotropic diffusion. The results show more accuracy compared to the previous method and significant improvement in the processing time.

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Acknowledgments

The authors would like to thank M. Uhercik and J. Kybic for providing the access to the 3D ultrasound data.

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Authors and Affiliations

  1. Amirkabir University of Technology, Tehran, Iran

    Leila Malekian, Heidar Ali Talebi & Farzad Towhidkhah

Authors
  1. Leila Malekian
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  2. Heidar Ali Talebi
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  3. Farzad Towhidkhah
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Corresponding author

Correspondence to Leila Malekian .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Mansour Jamzad

  3. Sharif University of Technology, Tehran, Iran

    Hossein Asadi

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© 2014 Springer International Publishing Switzerland

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Malekian, L., Talebi, H.A., Towhidkhah, F. (2014). Needle Detection in 3D Ultrasound Images Using Anisotropic Diffusion and Robust Fitting. In: Movaghar, A., Jamzad, M., Asadi, H. (eds) Artificial Intelligence and Signal Processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-10849-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-10849-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10848-3

  • Online ISBN: 978-3-319-10849-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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