Fuzzy C-means techniques for medical image segmentation

Huiyu Zhou*, Gerald Schaefer, Chunmei Shi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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)
PublisherSpringer
Pages257-271
Number of pages15
ISBN (Electronic)978-3-540-89968-6
ISBN (Print)978-3-540-89967-9
DOIs
Publication statusPublished - Jan 2009

Publication series

NameStudies in Fuzziness and Soft Computing
PublisherSpringer
Volume242
ISSN (Print)1434-9922

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  • Cite this

    Zhou, H., Schaefer, G., & Shi, C. (2009). Fuzzy C-means techniques for medical image segmentation. In Y. Jin, & L. Wang (Eds.), Fuzzy systems in bioinformatics and computational biology (pp. 257-271). (Studies in Fuzziness and Soft Computing; Vol. 242). Springer. https://doi.org/10.1007/978-3-540-89968-6_13