Image segmentation is considered an important step in image processing.Fuzzy C-means clustering(FCM)is one of the commonly used methods in image segmentation.In order to overcome the shortcomings of fuzzy c-means clustering algorithm,which is prone to falling into local optima,this algorithm combines fuzzy c-means clustering with quantum particle swarm optimization algorithm based on the theory of neutrosophy.Firstly,according to the fuzzy theory of neutrosophy,convert the image into neutrosophic image,and then use α The mean and image enhancement algorithms preprocess the neutrosophic image,and finally use the QPSO-FCM algorithm for segmentation.In the experiment,both natural images and medical images were used to validate these methods.Regardless of whether noise was added to the images,their segmentation boundaries were clearer compared to other algorithms.
维普
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