Summary
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.
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Zhou, H., Schaefer, G., Shi, C. (2009). Fuzzy C-Means Techniques for Medical Image Segmentation. In: Jin, Y., Wang, L. (eds) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_13
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DOI: https://doi.org/10.1007/978-3-540-89968-6_13
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