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An Adaptive Fuzzy Segmentation Algorithm for Three-Dimensional Magnetic Resonance Images

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Book cover Information Processing in Medical Imaging (IPMI 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1613))

Abstract

An algorithm is proposed for the fuzzy segmentation of two and three-dimensional multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the two-dimensional adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new, faster multigrid-based algorithm for its implementation. We show using simulated MR data that 3-D AFCM yields significantly lower error rates than both the standard fuzzy C-means algorithm and several other competing methods when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

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© 1999 Springer-Verlag Berlin Heidelberg

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Pham, D.L., Prince, J.L. (1999). An Adaptive Fuzzy Segmentation Algorithm for Three-Dimensional Magnetic Resonance Images. In: Kuba, A., Šáamal, M., Todd-Pokropek, A. (eds) Information Processing in Medical Imaging. IPMI 1999. Lecture Notes in Computer Science, vol 1613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48714-X_11

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  • DOI: https://doi.org/10.1007/3-540-48714-X_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66167-2

  • Online ISBN: 978-3-540-48714-2

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