Abstract：Objective An improved Fuzzy C-means (FCM) clustering algorithm based on neighborhood membership parameter
optimization was proposed for MR image segmentation in the brain. Methods First, genetic algorithms (GAs) and particle swarm
optimization (PSO) were combined to determine the optimum value of degree of attraction. Then the similarity function was modified
by neighborhood attraction. Finally, according to the improved FCM clustering algorithm, brain MR images were segmented. Image
segmentation accuracy was measured by the percentage of negative false, positive false, total false, and similarity index. Results
Both simulated and real MR images with noise were evaluated by different FCM-based methods. Qualitative analysis showed that
edges and small structures were well preserved with the proposed method. Quantitative evaluation results showed that the percentage
of negative false (0.0058%-4.28%), positive false (0.0182%-20.15%) and total false (0.0085%-7.15%) were all the minimum, and the
similarity index (92.65%) was the largest in the all improved FCM clustering algorithms. Conclusion The combination of genetic
algorithm and particle swarm optimization algorithm can obtain the optimal membership parameters. The improved FCM clustering
algorithm can effectively overcome the limitations caused by noise and improve the accuracy of MR image segmentation in the brain,
which has a high clinical application value.
宋方奔，缪正飞，张子齐. 基于模糊C均值聚类改进算法的脑部MR图像分割研究[J]. 中国医疗设备, 2019, 34(2): 71-75.
SONG Fangben, MIAO Zhengfei, ZHANG Ziqi. Study on Brain MR Image Segmentation Based on Fuzzy C-Means Clustering Algorithm
SONG Fangben, MIAO Zhengfei, ZHANG Ziqi. China Medical Devices, 2019, 34(2): 71-75.
