Paper Info
Title
Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering.
Abstract
An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
Year
DOI
Venue
2015
10.1155/2015/485495
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Kernel (linear algebra),Computer vision,Fuzzy clustering,Median filter,Segmentation,Computer science,Euclidean distance,Artificial intelligence,Cluster analysis,Grayscale,Machine learning,Kernel (statistics)
Journal
2015
ISSN
Citations 
PageRank 
1748-670X
16
0.65
References 
Authors
13
6
Name
Order
Citations
PageRank
Ahmed Elazab1517.28
Changmiao Wang2241.33
Fucang Jia3888.63
Jianhuang Wu46011.75
Guanglin Li531457.23
Qingmao Hu616019.73