Skip to main content
SpringerLink
Log in

A Machine Learning Approach Towards Fatty Liver Disease Detection in Liver Ultrasound Images

  • Conference paper
  • First Online:
Book cover Bildverarbeitung für die Medizin 2021

Part of the book series: Informatik aktuell ((INFORMAT))

  • 1630 Accesses

Abstract

Fatty liver disease (FLD) is one of the prominent diseases which affects the normal functionality of the liver by building vacuoles of fat in the liver cells. FLD is an indicator of imbalance in the metabolic system and could cause cardiovascular diseases, liver inflammation, cirrhosis and furthermore neoplasm. Detection and specification of a FLD are bene

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Langer SG, Carter SJ, Haynor DR, et al. Image acquisition: ultrasound, computed tomography, and magnetic resonance imaging. World J Surg. 2001;25:1428–1437.

    Google Scholar 

  2. Fu M, D S, Hussain. Automated classification of liver disorders using ultrasound images. J Med Syst. 2012;36(5):3163–3172.

    Google Scholar 

  3. Kyriacou E, Pavlopoulos S, et al. Computer assisted characterization of diffused liver disease using image texture analysis techniques on B-scan images. IEEE Nucl Sci Symp Conf Rec (1997). 1997;2:1479–1483.

    Google Scholar 

  4. Kyriacou E, Pavlopoulos S, et al. Fuzzy neural network-based texture analysis of ultrasonic images. IEEE Eng Med Biol Mag. 2000;19:39–47.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science, University of Magdeburg, Magdeburg, Deutschland

    Adarsh Kuzhipathalil, Anto Thomas & Keerthana Chand

  2. INKA-Application Driven Research, University of Magdeburg, Magdeburg, Deutschland

    Elmer Jeto Gomes Ataide, Michael Friebe & Jens Ziegle

  3. Department of Simulation and Graphics, University of Magdeburg, Magdeburg, Deutschland

    Annika Niemann & Sylvia Saalfeld

  4. Department of Gastroenterology, Hepatology and Infectious Diseases, University of Magdeburg, Magdeburg, Deutschland

    Alexander Link

  5. STIMULATE Research Campus, University of Magdeburg, Magdeburg, Deutschland

    Annika Niemann & Sylvia Saalfeld

Authors
  1. Adarsh Kuzhipathalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anto Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keerthana Chand
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elmer Jeto Gomes Ataide
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Annika Niemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sylvia Saalfeld
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael Friebe
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jens Ziegle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adarsh Kuzhipathalil .

Editor information

Editors and Affiliations

  1. Fakultät für Informatik und Mathematik, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany

    Christoph Palm

  2. Peter L. Reichertz Institut für Medizinische Informatik der TU Braunschweig und der Medizinischen Hochschule Hannover, Braunschweig, Germany

    Thomas M. Deserno

  3. Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany

    Heinz Handels

  4. Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität, Erlangen, Germany

    Andreas Maier

  5. Medical Image Computing, E230, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Klaus Maier-Hein

  6. Institut für Medizinische Informatik, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Thomas Tolxdorff

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kuzhipathalil, A. et al. (2021). A Machine Learning Approach Towards Fatty Liver Disease Detection in Liver Ultrasound Images. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_21

Download citation

Publish with us

Policies and ethics

Search

Navigation