Ensemble of fuzzy c-means clustering algorithm based on cluster label uniformity for MRI brain image segmentation

This paper proposes a novel ensemble method with multiple runs of the fuzzy c-means (FCM) clustering algorithm as the base learners. However, the major challenge in the ensemble of different runs of FCM lies in the labeling of clusters in each run. As there is no uniformity for numbering the cluster labels, the data points belonging to close clusters may be assigned to different clusters across multiple runs of FCM. Hence, assembling the cluster solutions of different runs of FCM becomes difficult. This challenge has been addressed in this study by proposing a novel method to ensemble multiple runs of FCM. In the proposed method, the cluster labels of different runs are renumbered based on proximity among the clusters, making it easy to ensemble cluster solutions in multiple runs of FCM. The class labels of the non-ensemble data points are determined by using three state-of-the-art classifiers, namely, k-nearest neighbors, support vector machine, and artificial neural network. The proposed method is applied for segmenting magnetic resonance imaging (MRI) of the human brain. The division of MRI brain images into distinct tissue classes is a crucial aspect of neurological disease and clinical research. The performance of the proposed method in segmenting the MRI brain images is compared to the Gaussian mixture models based on the values of three performance measures, namely, the Rand index, adjusted Rand index, and Minkowski score. The simulation results demonstrate the supremacy of the proposed method over the Gaussian mixture models.