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The paper presents a method for spatial fuzzy clustering (SFC) via Markov Random Fields (MRF) for the detection of brain activation regions in Functional Magnetic Resonance Imaging (fMRI) statistical parametric maps (SPMs) to improve the accuracy of the detection of such regions. The fMRI SPM is assumed to be an MRF and we define a fuzzy neighborhood energy function to describe the interaction between neighboring voxels. The final labeling is determined by a joint fuzzy membership. We compare the proposed spatial fuzzy clustering technique with the usual voxel-wise thresholding, traditional fuzzy clustering and Contextual Clustering (CC) [E. Salli, H.J. Aronen, S. Savolainen, A. Korvenoja, A. Visa, Contextual clustering for analysis of functional MRI data, IEEE Transactions on Medical Imaging 20 (2001) 403–414]. Experiments based on synthetic and real fMRI data demonstrate that the clustering performance of our method is significantly better than both simple thresholding and conventional non-spatial fuzzy clustering techniques. Our experiments also show that in relatively high quality SPMs (contrast to noise ratio ( CNR ) > 2.5 ), the performance of SFC and CC is very similar. In the case of the simulated datasets, when the SPMs have poor quality ( CNR 2.5 ), our method outperforms CC in reducing false positives and improving classification accuracy. |
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