Title: Automatic brain MRI segmentation scheme based on feature weighting factors selection on fuzzy c-means clustering algorithms with Gaussian smoothing

Authors: Kai Xiao, Sooi Hock Ho, Andrzej Bargiela

Addresses: School of Computer Science, Faculty of Science, University of Nottingham, Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor, Malaysia. ' School of Computer Science, Faculty of Science, University of Nottingham, Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor, Malaysia. ' School of Computer Science, Faculty of Science, University of Nottingham, Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor, Malaysia

Abstract: In this paper we introduce a new clustering method and apply it to brain magnetic resonance imaging (MRI) lateral ventricular compartments segmentation. The method uses Gaussian smoothing to enable fuzzy c-mean (FCM) to create both a more homogeneous clustering result and reduce effect caused by noise. With the objective of finding the optimal clustering results, we present a weighted clustering scheme which is applied to a Gaussian smoothed image using bootstrapping approach of feature weighting. The scheme is called weighted FCM with Gaussian smoothing (WGFCM). In addition to the observations on the clustering results of the MR images, we use validity functions and clustering centroids to evaluate the clustering results. Compared with the standard FCM with or without Gaussian smoothing, we found that the proposed scheme provides a better clustering performance for brain MRI lateral ventricular compartments segmentation.

Keywords: fuzzy image processing; clustering; fuzzy c-means; weighting factors selection; Gaussian smoothing; clustering validity functions; bioinformatics; automatic segmentation; brain MRI segmentation; feature weighting; magnetic resonance imaging.

DOI: 10.1504/IJCIBSB.2010.031393

International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 2010 Vol.1 No.3, pp.316 - 331

Published online: 02 Feb 2010 *

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