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Fuzzy image segmentation using membership connectedness

Kasaei, S ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1155/2008/417293
  3. Publisher: 2008
  4. Abstract:
  5. 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
  6. Keywords:
  7. Digital image storage ; Fuzzy clustering ; Fuzzy systems ; Image processing ; Magnetic resonance imaging ; Pixels ; Data sets ; Feature informations ; Functional forms ; Fuzzy connectednesses ; Fuzzy image segmentations ; Magnetic resonance images ; Medical image applications ; Membership connectednesses ; Noisy images ; Segmentation algorithms ; Spatial relations ; Spatial spaces ; Synthetic images ; Image segmentation
  8. Source: Eurasip Journal on Advances in Signal Processing ; Volume 2008 , 2008 ; 16876172 (ISSN)
  9. URL: https://asp-eurasipjournals.springeropen.com/articles/10.1155/2008/417293