IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i18p2287-d637229.html
   My bibliography  Save this article

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

Author

Listed:
  • Jorge Munoz-Minjares

    (Department of Electrical Engineering, Universidad Autónoma de Zacatecas “Campus Jalpa”, Libramiento Jalpa Km. 156+380, Zacatecas 99601, Mexico)

  • Osbaldo Vite-Chavez

    (Department of Electrical Engineering, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde 801, Zacatecas 98000, Mexico)

  • Jorge Flores-Troncoso

    (Department of Electrical Engineering, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde 801, Zacatecas 98000, Mexico)

  • Jorge M. Cruz-Duarte

    (Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico)

Abstract

Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

Suggested Citation

  • Jorge Munoz-Minjares & Osbaldo Vite-Chavez & Jorge Flores-Troncoso & Jorge M. Cruz-Duarte, 2021. "Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians," Mathematics, MDPI, vol. 9(18), pages 1-19, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2287-:d:637229
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/18/2287/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/18/2287/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Saralees Nadarajah, 2005. "A generalized normal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 685-694.
    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. Branislav Panić & Marko Nagode & Jernej Klemenc & Simon Oman, 2022. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks," Mathematics, MDPI, vol. 10(22), pages 1-22, November.

    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. García, V.J. & Gómez-Déniz, E. & Vázquez-Polo, F.J., 2010. "A new skew generalization of the normal distribution: Properties and applications," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 2021-2034, August.
    2. Müller K. & Richter W.-D., 2016. "Exact distributions of order statistics of dependent random variables from ln,p-symmetric sample distributions, n ∈ {3,4}," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-29, February.
    3. Sandi Baressi Šegota & Nikola Anđelić & Mario Šercer & Hrvoje Meštrić, 2022. "Dynamics Modeling of Industrial Robotic Manipulators: A Machine Learning Approach Based on Synthetic Data," Mathematics, MDPI, vol. 10(7), pages 1-17, April.
    4. Xin Chen & Zhangming Shan & Decai Tang & Biao Zhou & Valentina Boamah, 2023. "Interest rate risk of Chinese commercial banks based on the GARCH-EVT model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    5. Kapla, Daniel & Fertl, Lukas & Bura, Efstathia, 2022. "Fusing sufficient dimension reduction with neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    6. Lassance, Nathan & Vrins, Frédéric, 2023. "Portfolio selection: A target-distribution approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 302-314.
    7. Tran, Quang Van & Kukal, Jaromir, 2022. "A novel heavy tail distribution of logarithmic returns of cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    8. Jayles, Bertrand & Escobedo, Ramon & Cezera, Stéphane & Blanchet, Adrien & Kameda, Tatsuya & Sire, Clément & Théraulaz, Guy, 2020. "The impact of incorrect social information on collective wisdom in human groups," IAST Working Papers 20-106, Institute for Advanced Study in Toulouse (IAST).
    9. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.
    10. Li, Liuling & Mizrach, Bruce, 2010. "Tail return analysis of Bear Stearns' credit default swaps," Economic Modelling, Elsevier, vol. 27(6), pages 1529-1536, November.
    11. Mijeong Kim & Yanyuan Ma, 2019. "Semiparametric efficient estimators in heteroscedastic error models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(1), pages 1-28, February.
    12. Roger W. Barnard & Kent Pearce & A. Alexandre Trindade, 2018. "When is tail mean estimation more efficient than tail median? Answers and implications for quantitative risk management," Annals of Operations Research, Springer, vol. 262(1), pages 47-65, March.
    13. Punzo, Antonio & Bagnato, Luca, 2021. "Modeling the cryptocurrency return distribution via Laplace scale mixtures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    14. Zhou, Tong & Peng, Yongbo, 2020. "Adaptive Bayesian quadrature based statistical moments estimation for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    15. Bertrand Jayles & Ramon Escobedo & Stéphane Cezera & Adrien Blanchet & Tatsuya Kameda & Clément Sire & Guy Théraulaz, 2020. "The impact of incorrect social information on collective wisdom in human groups," Post-Print hal-03019820, HAL.
    16. Robert Paige & A. Trindade & R. Wickramasinghe, 2014. "Extensions of saddlepoint-based bootstrap inference," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(5), pages 961-981, October.
    17. Karol I. Santoro & Héctor J. Gómez & Inmaculada Barranco-Chamorro & Héctor W. Gómez, 2022. "Extended Half-Power Exponential Distribution with Applications to COVID-19 Data," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
    18. Liu, Xiaochun, 2019. "On tail fatness of macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 62(C).
    19. Merchant, Sandra M. & Nagata, Wayne, 2011. "Instabilities and spatiotemporal patterns behind predator invasions with nonlocal prey competition," Theoretical Population Biology, Elsevier, vol. 80(4), pages 289-297.
    20. Martín, J. & Pérez, C.J., 2009. "Bayesian analysis of a generalized lognormal distribution," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1377-1387, February.

    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:gam:jmathe:v:9:y:2021:i:18:p:2287-:d:637229. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.