Abstract：Objective This paper proposed an improved fuzzy C-mean clustering (FCM) algorithm which was used for the segmentation of
brain MR image. Methods Firstly, the maximum distance measure was used to select the initial clustering center of FCM. Then, the
membership function was constructed by updating the clustering center and spatial neighborhood information with hard classification
method. Finally, the image regions were classified. Results Artificial synthetic images and clinical brain MR images were used for
experiments. The results showed that the image noise level of SFCM/SFFCM algorithm based on spatial information was lower than that
of traditional FCM/FFCM algorithm. Quantitative analysis showed that the fuzzy partition coefficient Vpc (0.944) and partition entropy Vpe
(0.043) of classification evaluation index based on SFCM1,1/SFFCM1,1 were optimal. Compared with standard FCM, program of SFFCM1,1 consumed
37.2%~82.9% less time and reduced the number of iterations by 5~20 times. Conclusion The SFFCM segmentation algorithm proposed in
this study has faster convergence speed and higher accuracy, which is a feasible brain MR image segmentation algorithm.
任彤. 基于快速模糊C均值和邻域空间信息的脑部MR图像分割[J]. 中国医疗设备, 2019, 34(9): 93-95.
REN Tong. Brain MR Image Segmentation Based on Fast Fuzzy C-Means with Neighborhood Spatial Information. China Medical Devices, 2019, 34(9): 93-95.
