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Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique

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Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 456)


Seeded Region Growing algorithm is observed to be successfully implemented as a segmentation technique of medical images. This algorithm starts by selecting a seed point and, growing seed area through the exploitation of the fact that pixels which are close to each other have similar features. To improve the accuracy and effectiveness of region growing segmentation, some works tend to automate seed selection step. In this paper, we present a comparative study of two automatic seed selection methods for breast tumor detection using seeded region growing segmentation. The first method is based on thresholding technique and the second method is based on features similarity. Each method is applied on two modalities of breast digital images. Our results show that seed selection method based on thresholding technique is better than seed selection method based on features similarity.


  • Medical image segmentation
  • Medical informatics
  • Automatic seed selection
  • Region growing
  • Tumor detection


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Correspondence to Ahlem Melouah .

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Melouah, A. (2015). Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19577-3

  • Online ISBN: 978-3-319-19578-0

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