A New Brain MRI Image Segmentation Strategy Based on K-means Clustering and SVM

摘要

For the problem of noise and no reference image during brain magnetic resonance imagery (MRI) image segmentation, this paper proposes a new strategy to segment brain MRI image based on K-means clustering algorithm and support vector machine (SVM). Firstly, the strategy segments brain MRI image using K-means clustering algorithm to obtain the initial classification result as the class label, secondly, the feature vectors of each pixel of brain tissue are selected as the training samples and test samples, finally, brain MRI image is segmented by SVM. Experimental results show that the proposed segmentation strategy obtains better segmentation effect, especially has a good noise suppression for brain images with low signal-noise-ratio (SNR).