Fuzzy C-mean Clustering Based on Improved Local Information for MR Image Segmentation
In order to ensure that the fuzzy C-means(FCM)can achieve better segmentation results when segmenting MRI images,the neighborhood item is improved under the framework of the fuzzy C-means algorithm based on local information(FLICM),and a new FLICM algorithm is proposed.First,the additional images is generated by non-local mean filtering on original image.The pixel consistency coefficient is defined according to the additional image information and the original image information,and the noise level of pixel points is measured to avoid the excessive influence of the noisy pixel on the field items.Then,a difference coefficient between the center pixel and the neighborhood pixel is constructed to measure the gray difference of the neighborhood pixel,and the pixel correlation coefficient is established in combination with the pixel consistency coefficient,which can calculate the correlation of the neighborhood pixels more effectively.Finally,a new fuzzy factor is constructed with pixel correlation coefficient and combined with FLICM algorithm to obtain an improved FLICM algorithm.Compared with the three algorithms on different images,the proposed algorithm can get more accurate segmentation results.
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