Intensity inhomogeneity correction and segmentation of magnetic resonance images using a multi-stage fuzzy clustering approach

Sándor M. Szilágyi, László Szilágyi, David Iclǎnzan, László Ddvid, Attila Frigy, Zoltán Benyó

Research output: Article

2 Citations (Scopus)

Abstract

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms, and they generally have difficulties when INU reaches high amplitudes. This study proposes a multiple stage fuzzy c-means (FCM) clustering based algorithm for the estimation and compensation of INU, by modelling it as a slowly varying additive or multiplicative noise. The slowly varying behaviour of the bias or gain field is assured by a smoothing filter that performs a context dependent averaging, controlled by a morphological criterion. The segmentation is also supported by a prefiltering technique for Gaussian and impulse noise elimination. The experiments using 2-D synthetic phantoms and real MR images indicate that the proposed method provides accurate segmentation. The resulting segmentation and fuzzy membership values can serve as excellent support for 3-D registration and surface reconstruction techniques.

Original languageEnglish
Pages (from-to)513-528
Number of pages16
JournalNeural Network World
Volume19
Issue number5
Publication statusPublished - dec. 18 2009

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Hardware and Architecture
  • Artificial Intelligence

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