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Spatial Fuzzy Clustering and Its Application for MRI and CT Image Segmentation

Due to the low segmentation accuracy and sensitivity to initial contour in image segmentation of CV model, an image segmentation algorithm based on CV model combined with spatial fuzzy c-means was proposed for MRI and CT image segmentation with unclear boundary, artifact and high noise.
 Based on the rough segmentation of the image by using the fuzzy c-means clustering algorithm in the spatial domain, the initial contour is set by using the clustering information to assist the CV model, and the target region is segmented by iterative evolution. The experimental results showed
 that when the number of iterations was only 50, the Dice coefficient of our algorithm for segmentation of brain MRI images was 89.17%, 38.9% higher than the traditional CV model. It can be seen that the algorithm has higher discrimination and better segmentation effect for medical images.