Kernel-based Centroid Neural Network with Spatial Constraints for Image Segmentation

A Kernel-based Centroid Neural Network with spatial Constraints (K-CNN-S) is proposed and presented in this paper The proposed K-CNN-S is based on the Centroid Neural Networ (CNN) and also exploits advantages of the kernel method for mapping input data into a higher dimensional feature space. Furthermore, The K-CNN-S adopts the spatial constraints to reduce noise in images. The Magnetic Resonance Image (MRI) segmentation is performed to illustrate the application of the proposed K-CNN-S algorithm. Experiments and results on MRI data from Internet Brain Segmentation Repository(IBSR) demonstrate that image segmentation scheme based on the proposed K-CNN-S outperforms conventional algorithms including Fuzzy C-Means(FCM), Kernel-based Fuzzy C-Mean(K-FCM), and Kernel-based Fuzzy C-Mean with spatial constraints(K-FCM-S).