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Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling

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Abstract

Object quantification requires an image segmentation to make measurements about size, material composition and morphology of the object. In vector-valued or multispectral images, each image channel has its signal characteristics and provides special information that may improve the results of image segmentation method. This paper presents a region-based active contour model for vector-valued image segmentation with a variational level set formulation. In this model, the local image intensities are characterized using Gaussian distributions with different means and variances. Furthermore, by utilizing Markov random field, the spatial correlation between neighboring pixels and voxels is modeled. With incorporation of intensity nonuniformity model, our method is able to deal with brain tissue segmentation from multispectral magnetic resonance (MR) images. Our experiments on synthetic images and multispectral cerebral MR images with different noise and bias level show the advantages of the proposed method.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Zahra Shahvaran, Kamran Kazemi & Mohamad Sadegh Helfroush

Authors
  1. Zahra Shahvaran
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  2. Kamran Kazemi
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  3. Mohamad Sadegh Helfroush
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Correspondence to Kamran Kazemi.

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Shahvaran, Z., Kazemi, K. & Helfroush, M.S. Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling. SIViP 10, 887–893 (2016). https://doi.org/10.1007/s11760-015-0836-7

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  • DOI: https://doi.org/10.1007/s11760-015-0836-7

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