A robust fuzzy clustering algorithm using spatial information combined with local membership filtering for brain MR images

2020 ◽  
Author(s):  
Lanting Li ◽  
Peng Cao ◽  
Jinzhu Yang ◽  
Dazhe Zhao ◽  
Osmar R. Zaiane
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Mr Images ◽  
Fuzzy Clustering Algorithm ◽  
Brain Mr Images

scholarly journals Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3285 ◽  
Author(s):  
Hang Zhang ◽  
Jian Liu ◽  
Lin Chen ◽  
Ning Chen ◽  
Xiao Yang
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Clustering ◽  
Highly Correlated

Due to the limitation of the fixed structures of neighborhood windows, the quality of spatial information obtained from the neighborhood pixels may be affected by noise. In order to compensate this drawback, a robust fuzzy c-means clustering with non-neighborhood spatial information (FCM_NNS) is presented. Through incorporating non-neighborhood spatial information, the robustness performance of the proposed FCM_NNS with respect to the noise can be significantly improved. The results indicate that FCM_NNS is very effective and robust to noisy aliasing images. Moreover, the comparison of other seven roughness indexes indicates that the proposed FCM_NNS-based F index can characterize the aliasing degree in the surface images and is highly correlated with surface roughness (R2 = 0.9327 for thirty grinding samples).

Sign in / Sign up

Export Citation Format

Share Document