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Multiresolution adaptive K-means algorithm for segmentation of brain MRI

  • Session IA2b — Biomedical Imaging
  • Conference paper
  • First Online:
Image Analysis Applications and Computer Graphics (ICSC 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1024))

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Abstract

Segmentation of MR brain scans has received an enormous amount of attention in the medical imaging community over the past several years. In this paper we propose a new and general segmentation algorithm involving 3D adaptive K-Means clustering in a multiresolution wavelet basis. The voxel image of the brain is segmented into five classes namely, cerebrospinal fluid, gray matter, white matter, bone and background (remaining pixels). The segmentation problem is formulated as a maximum a posteriori (MAP) estimation problem wherein, the prior is assumed to be a Markov Random Field (MRF). The MAP estimation is achieved using an iterated conditional modes technique (ICM) in wavelet basis. Performance of the segmentation algorithm is demonstrated via application to phantom images as well as MR brain scans.

Support for the first author was partially provided by the Whitaker Foundation Award and for the second and third author from the NSF grant BCS-9396324 Manuscript submitted to the Intl. Compu. Sci. Conf. on Image Analysis and Compu. Graphics, Hong Kong, Dec. '95

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Author information

Authors and Affiliations

  1. Department of Computer & Information Sciences, University of Florida, 32611, Gainesville, FL

    B. C. Vemuri

  2. Department of Electrical & Computer Engineering, University of Florida, 32611, Gainesville, FL

    S. Rahman & J. Li

Authors
  1. B. C. Vemuri
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  2. S. Rahman
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  3. J. Li
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Editor information

Roland T. Chin Horace H. S. Ip Avi C. Naiman Ting-Chuen Pong

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© 1995 Springer-Verlag Berlin Heidelberg

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Vemuri, B.C., Rahman, S., Li, J. (1995). Multiresolution adaptive K-means algorithm for segmentation of brain MRI. In: Chin, R.T., Ip, H.H.S., Naiman, A.C., Pong, TC. (eds) Image Analysis Applications and Computer Graphics. ICSC 1995. Lecture Notes in Computer Science, vol 1024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60697-1_121

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  • DOI: https://doi.org/10.1007/3-540-60697-1_121

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60697-0

  • Online ISBN: 978-3-540-49298-6

  • eBook Packages: Springer Book Archive

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