A new brain MRI image segmentation strategy based on wavelet transform and K-means clustering

For the problem of low accuracy using K-means clustering algorithm to segment noisy brain magnetic resonance imaging (MRI) images, this paper proposed a strategy to improve segmentation accuracy. Firstly, the strategy uses wavelet transform to brain MRI image denoising, secondly, brain MRI image is segmented by k-means clustering algorithm. Experimental results show that the proposed strategy can effectively improve the segmentation accuracy of the noisy MRI brain image and is universal to some extent.