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Multiresolution adaptive K-means algorithm for segmentation of brain MRI

  • B. C. Vemuri
  • S. Rahman
  • J. Li
Session IA2b — Biomedical Imaging
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1024)

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.

Keywords

Segmentation Algorithm Markov Random Field Wavelet Basis Phantom Image Iterate Conditional Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • B. C. Vemuri
    • 1
  • S. Rahman
    • 2
  • J. Li
    • 2
  1. 1.Department of Computer & Information SciencesUniversity of FloridaGainesville
  2. 2.Department of Electrical & Computer EngineeringUniversity of FloridaGainesville

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