Efficient inhomogeneity compensation using fuzzy c-means clustering models

Authors:
Lá/Szló/ Szilá/Gyi;Sá/Ndor M. Szilá/Gyi;Balá/Zs Benyó/
Affiliations:
Sapientia University of Transylvania, Faculty of Technical and Human Sciences, Ş/oseaua Sighiş/oarei 1/C, 540485 Tí/rgu Mureş/, Romania11Tel.: +40 265 208 170/ fax: +40 265 206 211 ...;Sapientia University of Transylvania, Faculty of Technical and Human Sciences, Ş/oseaua Sighiş/oarei 1/C, 540485 Tí/rgu Mureş/, Romania11Tel.: +40 265 208 170/ fax: +40 265 206 211 ...;Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, Magyar tudó/sok krt. 2, H-1117 Budapest, Hungary22Tel.: +36 1 463 4027.
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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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 classification or clustering algorithms, they generally have difficulties when INU reaches high amplitudes and usually suffer from high computational load. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels. The theoretical assumptions are demonstrated using the fuzzy c-means algorithm, but the proposed modification is compatible with a various range of c-means clustering based INU compensation and MR image segmentation algorithms. Experiments carried out using synthetic phantoms and real MR images indicate that the proposed approach produces practically the same segmentation accuracy as the conventional formulation, but 20-30 times faster.