Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory

In recent decades, fuzzy segmentation methods, FCM algorithm in particular, have been widely employed for medical image segmentation, because they can save more information of the original image. But some artifacts like spatial noise and bias in medical images disturb segmentation results. This research presents an improved and robust FCM method based on information theoretic clustering, which estimated and corrected the heterogeneity of the magnetic field image (bias) and minimized the noise effects. To increase accuracy against any noise, the mutual information between data distribution of each cluster and those out of that cluster were maximized. The simulation results of the proposed algorithm are compared with previous fuzzy segmentation methods and its superiority in terms of segmentation of MR images in the database of brain images and synthetic images is illustrated.