Fiber, a Software for Classification and Counting in Histological Images

Erika Elizabeth Rodriguez-Torres (), Gonzalo Chávez-Fragoso, Enrique Vázquez-Mendoza, Cindy Xilonen Hinojosa-Rodríguez, Kenia López-García, Jorge Viveros-Rogel () and Ismael Jiménez-Estrada ()
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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

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 77-107 from Springer

Abstract: 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.

Keywords: Muscle fibers; ATPase; Skeletal muscle; Artificial intelligence; K-Means; Fuzzy C-Means; Kohonen self-organized maps (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-16368-4_6

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