IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-032-16368-4_6.html

Fiber, a Software for Classification and Counting in Histological Images

In: Handbook of Visual, Experimental and Computational Mathematics

Author

Listed:
  • Erika Elizabeth Rodriguez-Torres

    (Autonomous University of Hidalgo State (UAEH), Academic Area of Mathematics and Physics)

  • Gonzalo Chávez-Fragoso

    (National Polytechnic Institute, Department of Computer Science, Center for Research and Advanced Studies)

  • Enrique Vázquez-Mendoza

    (National Polytechnic Institute, Department of Physiology, Biophysics and Neuroscience, Center for Research and Advanced Studies)

  • Cindy Xilonen Hinojosa-Rodríguez

    (National Polytechnic Institute, Department of Physiology, Biophysics and Neuroscience, Center for Research and Advanced Studies)

  • Kenia López-García

    (Department of Chemical and Biological Sciences, University of the Americas Puebla)

  • Jorge Viveros-Rogel

    (Autonomous University of Hidalgo State (UAEH), Academic Area of Mathematics and Physics)

  • Ismael Jiménez-Estrada

    (National Polytechnic Institute, Department of Physiology, Biophysics and Neuroscience, Center for Research and Advanced Studies)

Abstract

Histochemical staining techniques are used to identify and classify cell phenotypes, such as skeletal muscle fibers. Currently, the identification and classification of muscle fibers are performed by an expert through visual inspection of histological images. This type of classification requires a considerable investment of time and resources. In order to overcome this obstacle, this software was developed, Fiber, which allows the automation of the classification of muscle fibers in a short time and with high efficiency. Fiber is implemented in Java, which guarantees multi-platform support, and uses artificial intelligenceArtificial intelligence data mining algorithms for pattern recognition, such as K-meansK-means, fuzzy c-meansFuzzy c-means, and Kohonen self-organized mapsKohonen self-organized maps. An expert-supervised method is also included in the software to complement the algorithms. The spreadsheet data can later be used to study the distribution patterns and organization of muscle fibers under both normal and pathological conditions. The results of the various analyses performed by the software are not only highly accurate, but the processing time is reduced by up to 90% compared to the processing time without the software. Expert assistance improves the performance of the software.

Suggested Citation

  • Erika Elizabeth Rodriguez-Torres & Gonzalo Chávez-Fragoso & Enrique Vázquez-Mendoza & Cindy Xilonen Hinojosa-Rodríguez & Kenia López-García & Jorge Viveros-Rogel & Ismael Jiménez-Estrada, 2026. "Fiber, a Software for Classification and Counting in Histological Images," Springer Books, in: Bharath Sriraman (ed.), Handbook of Visual, Experimental and Computational Mathematics, pages 77-107, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-16368-4_6
    DOI: 10.1007/978-3-032-16368-4_6
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:sprchp:978-3-032-16368-4_6. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.