Fuzzy clustering approach for brain tumor tissue segmentation in magnetic resonance images

  • Iván A. Rodríguez-Méndez
  • Raquel. Ureña
  • Enrique Herrera-ViedmaEmail author
Methodologies and Application


The early and accurate detection of brain tumors is key to improve the quality of life and the survival of cancer patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. Consequently, automatic and reliable segmentation methods are required. However, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this contribution, we present a new model of segmentation of brain magnetic resonance images. In order to obtain the region of interest, we propose a hybrid approach that carries out both fuzzy c-mean algorithm and multiobjective optimization taking into account both compactness and separation in the clusters with the purpose of improving the cluster center detection and speed up the convergence time. This new segmentation approach is a key component of the proposed magnetic resonance image-based classification system for brain tumors. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed approach using the DICOM MRI database.


Magnetic resonance images segmentation Tumor tissue detection Oncologic Fuzzy C-means clustering algorithm Cluster compelling information Region of interest extraction Optimization fuzzy compactness and separation Genetic algorithm 



This research is supported by PhD scholarship subprogram SENACYT-IFARHU (Secretaría Nacional de Ciencia, Tecnología e Innovación y el Instituto para la Formación y Aprovechamiento de Recursos Humanos), CN2070-2013-052, Panamá, Republic of Panama, the scholarship program COOPEN-Erasmus Mundus external cooperation windows: Polytechnic University of Valencia, Spain, the Autonomous University of Chiriqui, David, Panama. UNACHI (Universidad Autónoma de Chiriquí, David, Panamá) and FEDER funds (Grant number TIN2016-75850-R) and H2020-MSCA-IF Funds (Project DeciTrustNET ID: 746398). The authors would like to thank Ph.D. Del Fresno Mariana.

Compliance with ethical standards

Conflict of interest

This study was funded by SENACYT-IFARHU (Grant number CN2070-2013-052) and FEDER funds (Grant number TIN2016-75850-R) and H2020-MSCA-IF Funds (Project DeciTrustNET ID: 746398). Author Iván Ariel Rodríguez Méndez declares he has no conflict of interest. Author Raquel Ureña declares she has no conflict of interest. Author Enrique Herrera-Viedma declares he has no conflict of interest. This article does not contain any studies with human participants performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Iván A. Rodríguez-Méndez
    • 1
    • 2
  • Raquel. Ureña
    • 3
  • Enrique Herrera-Viedma
    • 1
    Email author
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.Department of Computer ScienceUniversidad Autónoma de Chiriquí-UNACHIDavidPanama
  3. 3.Institute of Artificial IntelligenceDe Montfort UniversityLeicesterUK

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