Fuzzy C-means techniques for medical image segmentation

Huiyu Zhou*, Gerald Schaefer, Chunmei Shi

*Corresponding author for this work

    Research output: Chapter in Book/Published conference outputChapter


    Segmentation is an important step in many medical imaging applications and a variety of image segmentation techniques exist. One group of segmentation algorithms is based on clustering concepts. In this chapter we provide an overview of several fuzzy c-means based clustering approaches and their application to medical imaging. In particular we evaluate the conventional hard c-means and fuzzy c-means (FCM) approches as well as three computationally more efficient derivatives of fuzzy c-means: Fast FCM with random sampling, fast generalised FCM, and a new anisotropic mean shift based FCM.

    Original languageEnglish
    Title of host publicationFuzzy systems in bioinformatics and computational biology
    EditorsYaochu Jin, Lipo Wang
    Place of PublicationBerlin (DE)
    Number of pages15
    ISBN (Electronic)978-3-540-89968-6
    ISBN (Print)978-3-540-89967-9
    Publication statusPublished - Jan 2009

    Publication series

    NameStudies in Fuzziness and Soft Computing
    ISSN (Print)1434-9922


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