research-article

Liver segmentation in MRI

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Computer Methods and Programs in Biomedicine, Volume 114, Issue 1

Pages 11 - 28

https://doi.org/10.1016/j.cmpb.2013.12.022

Published: 01 April 2014 Publication History

Abstract

There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.

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Cited By

Zhao YLi XWang WPan XYuan CGuan XWei D(2018)An Effective Segmentation Method for MRI Images Based on TV-L1 and GVF ModelJournal of Signal Processing Systems10.1007/s11265-017-1308-990:8-9(1205-1211)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11265-017-1308-9
Bereciartua APicon AGaldran AIriondo P(2016)3D active surfaces for liver segmentation in multisequence MRI imagesComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2016.04.028132:C(149-160)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.cmpb.2016.04.028
Göçeri EGürcan MDicle O(2014)Fully automated liver segmentation from SPIR image seriesComputers in Biology and Medicine10.1016/j.compbiomed.2014.08.00953:C(265-278)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1016/j.compbiomed.2014.08.009

Index Terms

Liver segmentation in MRI
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Shape inference
        Video segmentation
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection

Index terms have been assigned to the content through auto-classification.

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Copyright © Elsevier Ireland Ltd.

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Elsevier North-Holland, Inc.

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Publication History

Published: 01 April 2014

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Cited By

Zhao YLi XWang WPan XYuan CGuan XWei D(2018)An Effective Segmentation Method for MRI Images Based on TV-L1 and GVF ModelJournal of Signal Processing Systems10.1007/s11265-017-1308-990:8-9(1205-1211)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11265-017-1308-9
Bereciartua APicon AGaldran AIriondo P(2016)3D active surfaces for liver segmentation in multisequence MRI imagesComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2016.04.028132:C(149-160)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.cmpb.2016.04.028
Göçeri EGürcan MDicle O(2014)Fully automated liver segmentation from SPIR image seriesComputers in Biology and Medicine10.1016/j.compbiomed.2014.08.00953:C(265-278)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1016/j.compbiomed.2014.08.009

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