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Segmentation of the liver in ultrasound: a dynamic texture approach

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective

The segmentation of ultrasound (US) images is useful for several applications in computer aided interventions including the registration of pre-operative CT or MRI to intra-operative US. Shadowing, intensity inhomogeneity and speckle are the common effects on US images. They render the segmentation algorithms developed for other modalities inappropriate due to poor robustness. We present a novel method for classification of hepatic structures including vasculature and liver parenchyma on US images.

Methods

The method considers B-mode US images as a dynamic texture. The dynamics of each pixel are modelled as an auto regressive (AR) process perturbed with Gaussian noise. The linear coefficients and noise variance are estimated pixel-wise using Neumaier and Schneider’s algorithm. Together with mean intensity they comprise a parametric space in which classification (maximum a posteriori or K-nearest neighbour) of each pixel is performed. We emphasize the novelty of studying dynamics rather than static features such as intensity in the segmentation of various structures.

Results

We assessed the automatic segmentations of ten US sequences using Dice Similarity Coefficients. The algorithm’s capability of vessel extraction was tested on three sequences where Doppler US failed to capture vasculature.

Conclusion

The modelling of image dynamics with AR process combined with MAP classifier produced robust segmentation results indicating that the method has a good potential for intra-operative use.

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Author information

Authors and Affiliations

  1. Kongsberg SIM, Bygdøy Alle 5, 0257, Oslo, Norway

    Sergiy Milko

  2. University in Oslo, Intervensjonssenteret, Songsvannsveien 20, 0027, Oslo, Norway

    Eigil Samset

  3. Siemens Molecular Imaging, 23-38 Hythe Bridge Street, Oxford, OX1 2EP, UK

    Timor Kadir

Authors
  1. Sergiy Milko
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  2. Eigil Samset
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  3. Timor Kadir
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Corresponding author

Correspondence to Sergiy Milko.

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Milko, S., Samset, E. & Kadir, T. Segmentation of the liver in ultrasound: a dynamic texture approach. Int J CARS 3, 143–150 (2008). https://doi.org/10.1007/s11548-008-0217-6

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  • DOI: https://doi.org/10.1007/s11548-008-0217-6

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