Fuzzy Image Segmentation: An Automatic Unsupervised Method

  • Silvana G Dellepiane
  • Sonia Nardotto
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 15)


A new method is here proposed for the unsupervised, automatic, global region segmentation of digital images, hereinafter referred to as the “Automatic Fuzzy Segmentation” (AFS). Starting from the fuzzy intensity-connectedness definition (\(\chi \) -connectedness) and the related growing mechanism, it allows a strict and very simple integration between the analysis of topological connectedness and grey level similarities of the pixels belonging to the same region. By overcoming the previous drawback due to the need of some seed points selection, an iterative processing is here developed, able to adapt to the image content. The automatic selection of seed points is driven by intermediate connectedness results which alternates the analysis of inter-region similarities with inter-region separation measurements. The robustness of the method with respect to the three required parameters is discussed. Example cases related to real application domains are here presented and discussed.


Segmentation Fuzzy processing Connectedness MS lesion detection 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Università degli Studi di GenovaGenovaItaly

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