Multiple-Surface-Approximation-Based FCM With Interval Memberships for Bias Correction and Segmentation of Brain MRI

Fuzzy c-means (FCM) is a popular clustering method for image segmentation. However, FCM has difficulties in handling artifacts in brain magnetic resonance imaging (MRI), especially when it comes to bias field and noise. We propose a novel multiple-surface-approximation-based FCM with interval membership method for simultaneous bias correction and segmentation of Brain MRI. First, multiple surface representation of bias field is embedded into FCM to estimate and correct bias field. Then memberships of the improved FCM are extended to intervals. After the extension, clustering centers of different MR brain tissues could be solved more properly by the proposed method. Moreover, the proposed method is less sensitive to noise by introducing effects of neighboring pixels. Experiments conducted on artificial images and synthetic and real clinical Brain MRI show that the proposed method is effective and obtains better results of both bias field correction and segmentation than comparing methods.