Multi-stage FCM-Based Intensity Inhomogeneity Correction for MR Brain Image Segmentation

Authors:
László Szilágyi;Sándor M. Szilágyi;László Dávid;Zoltán Benyó
Affiliations:
Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary and Faculty of Technical and Human Science, Sapientia - Hungarian S ...;Faculty of Technical and Human Science, Sapientia - Hungarian Science University of Transylvania, Târgu-Mureş, Romania;Faculty of Technical and Human Science, Sapientia - Hungarian Science University of Transylvania, Târgu-Mureş, Romania;Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

Citing 4
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Abstract

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for 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 paper proposes a multiple stage fuzzy c-means (FCM) based algorithm for the estimation and compensation of INU, by modeling it as a slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothing filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The resulting segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.