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Fuzzy Image Segmentation Using Membership Connectedness


Fuzzy connectedness and fuzzy clustering are two well-known techniques for fuzzy image segmentation. The former considers the relation of pixels in the spatial space but does not inherently utilize their feature information. On the other hand, the latter does not consider the spatial relations among pixels. In this paper, a new segmentation algorithm is proposed in which these methods are combined via a notion called membership connectedness. In this algorithm, two kinds of local spatial attractions are considered in the functional form of membership connectedness and the required seeds can be selected automatically. The performance of the proposed method is evaluated using a developed synthetic image dataset and both simulated and real brain magnetic resonance image (MRI) datasets. The evaluation demonstrates the strength of the proposed algorithm in segmentation of noisy images which plays an important role especially in medical image applications.

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Correspondence to Shohreh Kasaei.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hasanzadeh, M., Kasaei, S. Fuzzy Image Segmentation Using Membership Connectedness. EURASIP J. Adv. Signal Process. 2008, 417293 (2008).

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  • Segmentation Algorithm
  • Spatial Relation
  • Brain Magnetic Resonance Image
  • Fuzzy Cluster
  • Noisy Image