An efficient approach to intensity inhomogeneity compensation using c-means clustering models

Authors:
László Szilágyi;David Iclănzan;Lehel Crăciun;Sándor Miklós Szilágyi
Affiliations:
Faculty of Technical and Human Science, Sapientia - Hungarian Science University of Transylvania, Tîrgu-Mureş, Romania;Faculty of Technical and Human Science, Sapientia - Hungarian Science University of Transylvania, Tîrgu-Mureş, Romania;Faculty of Technical and Human Science, Sapientia - Hungarian Science University of Transylvania, Tîrgu-Mureş, Romania;Faculty of Technical and Human Science, Sapientia - Hungarian Science University of Transylvania, Tîrgu-Mureş, Romania
Venue:
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2011

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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 clustering algorithms, and they generally have difficulties when INU reaches high amplitudes. 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 techniques. Experiments 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.