Segmentation of fMRI using pulse-coupled neural network with kernel PCA

The functional magnetic resonance imaging (fMRI) is an advanced medical imaging technique based on blood oxygen level dependence (BOLD), which has the higher time and the spatial resolution. Because the BOLD-fMRI signal changes only about 0.5-2%, how to examine and locate the functional activation signal from those pictures with low signal to noise ratio accurately and reliably is the open question. An image segmentation algorithm based on pulse-coupled neural network with kernel principal component analysis(PCA) is presented in this paper. The kernel PCA enables us to extract nonlinear features and remove outliers in data vectors and achieve dimension reduction. After that, a new image segmentation approach based on pulse coupled neural network (PCNN) is presented. PCNN dynamically evaluates similarity between any two samples owing to the outstanding centralization characteristic based on the vicinity in space and the comparability of brightness. It has higher accuracy and faster performance than those classical clustering algorithms. Experimental results with fMRI images have shown the effectiveness of the proposed algorithm.