Abstract When the fast generalized fuzzy C-means clustering(FGFCM]is directly used to segment serious noise images, the clustering center offset, inaccurate results and error of the image segmentation are easily caused due to the noise. Therefore, a fast generalized fuzzy C-means clustering algorithm based on adaptive filtering is proposed. Firstly, the parameter balance factor is adaptively determined according to the noise probability of nonlocal pixels to reflect the spatial structure information in the image more accurately. Then]the balance factor is used to effectively combine the linear weighted sum filtered image in the FGFCM algorithm with the median filtered image of the original image to create the adaptive filtered image. Since the filtering degree of the filtered image depends on the probability that the pixel is noise in the image, the dynamic noise suppression performance of the proposed method can be greatly improved. The experimental results show that compared with FCM and FGFCM, the proposed method obtains more accurate results in clustering segmentation of images with serious noise.
Fund:Supported by National Natural Science Foundation of China(No.61761027)
Corresponding Authors:
WANG Xiaopeng, Ph.D.professor. His research interests include image processing and analysis.
About author:: ZHANG Yongfang, master student. Her research interests include image analysis and understanding;WANG Wei, master student. His research interest include image processing;WEN Haotian, master student. His research interests include image analysis and understanding.
WANG Xiaopeng,ZHANG Yongfang,WANG Wei等. Image Segmentation Using Fast Generalized Fuzzy C-means Clustering Based on Adaptive Filtering[J]. , 2018, 31(11): 1040-1046.
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2018, Vol. 31 
