A Modified Kernel-based Fuzzy C-Means Algorithm For Images Segmentation / 医疗卫生装备

ABSTRACT

Objective To segment brain magnetic resonance (MR) images corrupted by noises. Methods We presented a novel Fuzzy C-Means (FCM) algorithm for image segmentation. The algorithm was by modifying the objective function in the conventional FCM. Firstly,by using kernel method,the original Euclidean distance in the FCM was replaced by a kernel-induced distance. Then,a spatial penalty term was added to the objective function to compensate the influence of the neighboring pixels on the center pixel. Results Segmentation results on a four-class synthetic image corrupted by salt & pepper noise shows that the new algorithm is less speckled and smoother. The new algorithm is applied to simulation MR images and is shown to have less misclassification rate than the other FCM-based methods. Conclusion The results of experiments show that the proposed algorithm is more robust to noise than other FCM-based methods.