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Parallel hesitant fuzzy C-means algorithm to image segmentation

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Abstract

Hesitant fuzzy information allows clustering data with multiple possible membership values for a single item in a reference set. Hesitant fuzzy sets have been applied in many decision-making problems, obtaining better results against others kinds of fuzzy sets. So, in this paper a method for image segmentation based on the hesitant fuzzy set theory is investigated. Additionally, processing time is sped up with a hardware-level parallelization technique using OpenMP. Comparing the experimental results, it can be seen that the segmentation by the propose algorithm is superior, compared to some of the state of the art. The most striking feature to emerge from this algorithm is its ability to preserve the details of the boundaries of the region, in addition to the fact that the regions are more homogeneous.

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Acknowledgements

The authors are grateful to the editors and the reviewers for their valuable comments. the authors thank the CONACYT, TecNM/CENIDET for their support of this research through the project “Clasificador para detectar fibrilación auricular en señales electrocardiográficas utilizando una red recurrente profunda entrenada con momentos de tiempo-frecuencia”

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Authors and Affiliations

  1. Tecnlógico Nacional de México/CENIDET, Cuernavaca, Morelos, Mexico

    Virna V. Vela-Rincón & Dante Mújica-Vargas

  2. Sección de Estudios de Posgrado e Investigación, Esime Azcapotzalco, Instituto Politécnico Nacional, Ciudad de México, Mexico

    Jose de Jesus Rubio

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  1. Virna V. Vela-Rincón
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  2. Dante Mújica-Vargas
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  3. Jose de Jesus Rubio
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Correspondence to Dante Mújica-Vargas.

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Vela-Rincón, V.V., Mújica-Vargas, D. & de Jesus Rubio, J. Parallel hesitant fuzzy C-means algorithm to image segmentation. SIViP 16, 73–81 (2022). https://doi.org/10.1007/s11760-021-01957-8

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