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Multinomial Level-Set Framework for Multi-region Image Segmentation

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Scale Space and Variational Methods in Computer Vision (SSVM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

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Abstract

We present a simple and elegant level-set framework for multi-region image segmentation. The key idea is based on replacing the traditional regularized Heaviside function with the multinomial logistic regression function, commonly known as Softmax. Segmentation is addressed by solving an optimization problem which considers the image intensities likelihood, a regularizer, based on boundary smoothness, and a pairwise region interactive term, which is naturally derived from the proposed formulation. We demonstrate our method on challenging multi-modal segmentation of MRI scans (4D) of brain tumor patients. Promising results are obtained for image partition into the different healthy brain tissues and the malignant regions.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering and the Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Tammy Riklin Raviv

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  1. Tammy Riklin Raviv
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Correspondence to Tammy Riklin Raviv .

Editor information

Editors and Affiliations

  1. University of Copenhagen , Copenhagen, Denmark

    François Lauze

  2. Technical University of Denmark , Kongens Lyngby, Denmark

    Yiqiu Dong

  3. Technical University of Denmark , Kongens Lyngby, Denmark

    Anders Bjorholm Dahl

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Riklin Raviv, T. (2017). Multinomial Level-Set Framework for Multi-region Image Segmentation. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_31

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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