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.
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Szilágyi, L., Szilágyi, S.M., Dávid, L., Benyó, Z. (2008). Multi-stage FCM-Based Intensity Inhomogeneity Correction for MR Brain Image Segmentation. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_55
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DOI: https://doi.org/10.1007/978-3-540-87559-8_55
Publisher Name: Springer, Berlin, Heidelberg
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