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A Selective Segmentation Model for Inhomogeneous Images

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Intelligent Computing Systems (ISICS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 820))

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Abstract

Automatic accurate detection and segmentation of all of the boundaries in a given image has been of an interest in the last decades. In contrast with global segmentation, where all the object boundaries in a given image are detected, for a large class of image segmentation tasks, only one object is required to be extracted. To successfully segment one single object, interactive/selective segmentation techniques has been delivered. However, the existing interactive/selective models cannot cope with images that have intensity inhomogeneity and presence of noise. In this paper, we propose a new variational level-set model which can deal with intensity inhomogeneity and presence of noise for the selective segmentation task. To evaluate the performance of our new model, we compare our results with the latest and state of the art models. Comparison of the proposed model with the latest and state of the art models show same efficiency and reliability on detecting objects/regions for homogeneous intensity images and outperforms when applied to inhomogeneous images with or without noise.

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Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, BAU, Istanbul, Turkey

    Lavdie Rada

  2. Department of Mathematics, University of Peshawar, Peshawar, Pakistan

    Haider Ali & Haroon Niaz Ali Khan

Authors
  1. Lavdie Rada
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  2. Haider Ali
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  3. Haroon Niaz Ali Khan
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Corresponding author

Correspondence to Lavdie Rada .

Editor information

Editors and Affiliations

  1. Autonomous University of Yucatán, Merida, Mexico

    Carlos Brito-Loeza

  2. Autonomous University of Yucatán, Merida, Mexico

    Arturo Espinosa-Romero

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Rada, L., Ali, H., Khan, H.N.A. (2018). A Selective Segmentation Model for Inhomogeneous Images. In: Brito-Loeza, C., Espinosa-Romero, A. (eds) Intelligent Computing Systems. ISICS 2018. Communications in Computer and Information Science, vol 820. Springer, Cham. https://doi.org/10.1007/978-3-319-76261-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-76261-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76260-9

  • Online ISBN: 978-3-319-76261-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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