Research On Fuzzy Image Clustering Algorithm Based On Particle Swarm Optimization Algorithm PSO In Ultrasound Image Segmentation

Medical ultrasound images are widely used in clinical treatment and diagnosis,because of its immediacy non-destructive repeatability high sensitivity.The ultrasound image segmentation become indispensable means while the extract information on specific tissues and organs.By image segmentation,medical ultrasound images can be separated from the desired anatomical structure or locate the position and shape of pathogens which is often directly determines the accuracy of the follow-up analysis of the treatment.However,due to the inherent medical ultrasound image speckle noise,organ tissue,and uncertainties that is related to the characteristics of texture image,the segmentation has not yet reached the requirements of clinical applications,which makes the division has been a problem.The thesis summarizes the importance of the image segmentation firstly in the image works and the significance of the current research in medical ultrasound image segmentation.Then,it reviewing the summary on the particle swarm optimization(PSO)and the Fuzzy C-means(FCM)clustering method.In order to get more stable and faster convergence in later iteration,the paper made improvements on the particle swarm algorithm.Due to the FCM is an unsupervised clustering and the fuzzy technology,it has already been widely used in image segmentation.However,the drawback is that the early convergence to local minimum.According to the well global search capability of the PSO and the well local search ability of FCM,the thesis combined both PSO and FCM together to achieve ultrasound image segmentation.The experimental results and the following analysis show that the method has a good performance to obtain the global optimal solution as well as presents better ultrasound image segmentation.