Multivariate image segmentation based on geometrically guided fuzzy C-means clustering

J.C. Noordam, W.H.A.M. van den Broek

    Research output: Contribution to journalArticleAcademicpeer-review

    25 Citations (Scopus)


    This paper describes a new approach to geometrically guided fuzzy clustering. A modified version of fuzzy C-means (FCM) clustering, conditional FCM, is extended to incorporate a priori geometrical information from the spatial domain in order to improve image segmentation. This leads to a new algorithm (GGC-FCM) where the cluster guidance is determined by the membership values of neighbouring pixels. GGC-FCM is tested on synthetic and real images to demonstrate the improved image segmentation compared to traditional FCM. Copyright (C) 2002 John Wiley Sons, Ltd.
    Original languageEnglish
    Pages (from-to)1-11
    JournalJournal of Chemometrics
    Issue number1
    Publication statusPublished - 2002


    • Multivariate imaging
    • Multispectral imaging
    • Segmentation
    • Fuzzy-C-means
    • Clustering

    Fingerprint Dive into the research topics of 'Multivariate image segmentation based on geometrically guided fuzzy C-means clustering'. Together they form a unique fingerprint.

  • Cite this