IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v466y2017icp521-536.html
   My bibliography  Save this article

Choosing parameters for Rényi and Tsallis entropies within a two-dimensional multilevel image segmentation framework

Author

Listed:
  • Ben Ishak, Anis

Abstract

In this work, the effect of Rényi and Tsallis entropies’ parameters on the image segmentation quality within a two-dimensional multilevel thresholding framework is assessed and analyzed. The problems of automatically tuning entropy’s parameter and determining the optimal thresholding values are solved in a single task. This is done by using the Quantum Genetic Algorithm (QGA). The numerical experiments conducted on different types of images demonstrated that Rényi and Tsallis entropies perform approximately similarly, and they are optimal when their parameters are null. Moreover, it was shown that optimizing the entropy does not lead to maximize the Peak Signal to Noise Ratio (PSNR) and the Structural SIMilarity (SSIM) criteria. Then, we have proved that these two criteria are not sufficiently consistent with human visual perception. Finally, the comparative study performed on some synthetic and real images demonstrated the effectiveness of the proposed method.

Suggested Citation

  • Ben Ishak, Anis, 2017. "Choosing parameters for Rényi and Tsallis entropies within a two-dimensional multilevel image segmentation framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 521-536.
  • Handle: RePEc:eee:phsmap:v:466:y:2017:i:c:p:521-536
    DOI: 10.1016/j.physa.2016.09.053
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116306781
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2016.09.053?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fabbri, Ricardo & Gonçalves, Wesley N. & Lopes, Francisco J.P. & Bruno, Odemir M., 2012. "Multi-q pattern analysis: A case study in image classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4487-4496.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Çelik, Gaffari & Talu, Muhammed Fatih, 2020. "Resizing and cleaning of histopathological images using generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fabbri, Ricardo & Bastos, Ivan N. & Neto, Francisco D. Moura & Lopes, Francisco J.P. & Gonçalves, Wesley N. & Bruno, Odemir M., 2014. "Multi-q pattern classification of polarization curves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 332-339.
    2. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.
    3. Amelia Carolina Sparavigna, 2015. "Tsallis Entropy In Bi-level And Multi-level Image Thresholding," International Journal of Sciences, Office ijSciences, vol. 4(01), pages 40-49, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:466:y:2017:i:c:p:521-536. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.