Automatic liver segmentation on Computed Tomography using random walkers for treatment planning

Authors

  • Mehrdad Moghbel Department of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Syamsiah Mashohor Department of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Rozi Mahmud Cancer Resource & Education Center, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • M. Iqbal Bin Saripan Department of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia

DOI:

https://doi.org/10.17179/excli2016-473

Keywords:

image segmentation, random walker, CT imaging, liver segmentation

Abstract

Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances with all these difficulties becoming more prominent in pathological livers. To achieve a more accurate segmentation, a random walker based framework is proposed that can segment contrast-enhanced livers CT images with great accuracy and speed. Based on the location of the right lung lobe, the liver dome is automatically detected thus eliminating the need for manual initialization. The computational requirements are further minimized utilizing rib-caged area segmentation, the liver is then extracted by utilizing random walker method. The proposed method was able to achieve one of the highest accuracies reported in the literature against a mixed healthy and pathological liver dataset compared to other segmentation methods with an overlap error of 4.47 % and dice similarity coefficient of 0.94 while it showed exceptional accuracy on segmenting the pathological livers with an overlap error of 5.95 % and dice similarity coefficient of 0.91.

Downloads

Published

2016-08-10

How to Cite

Moghbel, M., Mashohor, S., Mahmud, R., & Saripan, M. I. B. (2016). Automatic liver segmentation on Computed Tomography using random walkers for treatment planning. EXCLI Journal, 15, 500–517. https://doi.org/10.17179/excli2016-473

Issue

Vol. 15 (2016)

Section

Original articles

License

Authors who publish in this journal agree to the following terms:

  • The authors keep the copyright and grant the journal the right of first publication under the terms of the Creative Commons Attribution license, CC BY 4.0. This licencse permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
  • The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
  • Because the advice and information in this journal are believed to be true and accurate at the time of publication, neither the authors, the editors, nor the publisher accept any legal responsibility for any errors or omissions presented in the publication. The publisher makes no guarantee, express or implied, with respect to the material contained herein.
  • The authors can enter into additional contracts for the non-exclusive distribution of the journal's published version by citing the initial publication in this journal (e.g. publishing in an institutional repository or in a book).

Most read articles by the same author(s)