Title
Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation
Abstract
Although fuzzy c-means FCM algorithm and some of its variants have been extensively widely used in unsupervised medical image segmentation applications in recent years, they more or less suffer from either noise sensitivity or loss of details, which always is a key point to medical image processing. This paper presents a novel FCM variation method that is suitable for medical image segmentation. The proposed method, typically by incorporating multi-resolution bilateral filter which is combined with wavelet thresholding, provides the following advantages: 1 it is less sensitive to both high-and low-frequency noise and removes spurious blobs and noisy spots, 2 it yields more homogeneous clustering regions, and 3 it preserves detail, thus significantly improving clustering performance. By the use of synthetic and multiple-feature magnetic resonance MR image data, the experimental results and quantitative analyses suggest that, compared to other fuzzy clustering algorithms, the proposed method further enhances the robustness to noisy images and capacity of detail preservation.
Year
DOI
Venue
2013
10.4018/ijfsa.2013100104
IJFSA
Keywords
Field
DocType
image data,fuzzy clustering,fuzzy clustering algorithm,detail preservation,medical image processing,unsupervised medical image segmentation,medical image segmentation,multi-resolution bilateral filtering,novel fcm variation method,clustering performance,fuzzy c-means fcm algorithm,medical image
Fuzzy clustering,Computer vision,Pattern recognition,Computer science,Fuzzy logic,Segmentation-based object categorization,Image processing,Image segmentation,Robustness (computer science),Artificial intelligence,Cluster analysis,Bilateral filter
Journal
Volume
Issue
Citations 
3
4
2
PageRank 
References 
Authors
0.40
2
6
Name
Order
Citations
PageRank
Kai Xiao1152.96
Jianli Li220.73
Shuangjiu Xiao34114.18
Haibing Guan41106105.35
Fang Fang520.40
Aboul Ella Hassanien61610192.72