Abstract:Objective To proposed a novel fuzzy C-means clustering algorithm based on weighted pixel distance and relative
entropy for image segmentation. Methods First, the pixel to cluster-center distance was weighted using the reciprocal of the
local membership average. Second, the regulation term was formulated using the relative entropy divergence which measures the
proximity between a pixel membership and the local average of this membership in the immediate neighborhood. Finally, improved
FCM clustering algorithm was employed to segment synthetic and real-world images. Image segmentation results was measured
by misclassified pixels ratio, partition coefficient, partition entropy and Xie-Ben coefficient, and compared with the standard FCM,
a local data-based information FCM and a type of local membership information based FCM algorithms. Results Qualitative
analysis showed that the noise level was lowest, and contrast and definition were best with the proposed method. Quantitative
evaluation results showed that the measured indexes of proposed method outperformed other three FCM-based algorithms, in
which the simulated images misclassified pixels ratio was 0.09%±0.03%, partition coefficient (0.9986±0.0003), partition entropy
(0.0024±0.0009), and Xie-Ben coefficient (0.2114±0.0019). Conclusion The combination of weighted pixel distance and relative
entropy can effectively reduce image noise and improve segmentation accuracy, which has high clinical application value.
王薇,魏应敏. 基于加权像素距离和相对熵的模糊C均值聚类改进算法研究[J]. 中国医疗设备, 2020, 35(4): 71-74.
WANG Wei, WEI Yingmin. Improved Fuzzy C-Means Clustering Algorithm Based on
Weighted Pixel Distance and Relative Entropy for Image Segmentation. China Medical Devices, 2020, 35(4): 71-74.