Unsupervised segmentation of predefined shapes in multivariate images

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

    Research output: Contribution to journalArticleAcademicpeer-review

    9 Citations (Scopus)

    Abstract

    Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the spatially guided FCM (SG-FCM) algorithm is presented which segments multivariate images by incorporating both spatial and spectral information. Spatial information is described by a geometrical shape description and can vary from a local neighbourhood to a more extended shape model such as Hough circle detection. A modified FCM objective function uses the spatial information as described by the shape model. This results in a segmented image in which the construction of the cluster prototypes is influenced by spatial information. The performance of SG-FCM is compared with both FCM and the sequence of FCM and a majority filter. The SG-FCM segmented image shows more homogeneous regions and less spurious pixels.
    Original languageEnglish
    Pages (from-to)216-224
    JournalJournal of Chemometrics
    Volume17
    Issue number4
    DOIs
    Publication statusPublished - 2003

    Keywords

    • magnetic-resonance images
    • clustering-algorithm
    • fuzzy

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