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Fuzzy methods in medical imaging

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Abstract

Fuzzy sets theory is of great interest in medical image processing, for dealing with imprecise information and knowledge. It provides a consistent mathematical framework for knowledge representation, information modeling at different levels, fusion of heterogeneous information, reasoning and decision making. In this chapter, we provide an overview of the potential of this theory in medical imaging, in particular for classification, segmentation and recognition of anatomical and pathological structures.

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Authors and Affiliations

  1. Signal and Image Processing, Telecom ParisTech - CNRS LTCI, 46 rue Barrault, Paris, 75013, France

    I. Bloch

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  1. I. Bloch
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Editors and Affiliations

  1. Department of Applied Mathematics, École Centrale de Paris / INRIA Saclay, Île De France, Chatenay-Malabry, France

    Nikos Paragios

  2. Department of Biomedical Engineering, Diagnostic Radiology and Electrical Engineering, Yale University, New Haven, Connecticut, USA

    James Duncan

  3. INRIA Sophia Antipolis - Méditerranée, Sophia Antipolis Cedex, France

    Nicholas Ayache

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Bloch, I. (2015). Fuzzy methods in medical imaging. In: Paragios, N., Duncan, J., Ayache, N. (eds) Handbook of Biomedical Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09749-7_2

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