Paper Info
Title
A new fuzzy C-means method for magnetic resonance image brain segmentation
Abstract
In this paper, we introduce a new fuzzy c-means FCM method in order to improve the magnetic resonance images’ MRIs segmentation. The proposed method combines the FCM and possiblistic c-means PCM functions using a weighted Gaussian function. The weighted Gaussian function is given to indicate the spatial influence of the neighbouring pixels on the central pixel. The parameters of weighting coefficients are automatically determined in the implementation using the Gaussian function for every pixel in the image. The proposed method is realised by modifying the objective function of the PCM algorithm to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centres for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, that is, the compatibilities of the points with the class prototypes to overcome the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments using MRIs and comparison with other state-of-the-art algorithms. In the proposed method, the effect of noise is controlled by incorporating the possibility typicality function in addition to the membership function. Consideration of these constraints can greatly control the noise in the image as shown in our experiments.
Year
DOI
Venue
2015
10.1080/09540091.2014.970126
Connection Science
Keywords
Field
DocType
fuzzy c-means clustering,possiblistic c-means,medical image segmentation
Brain segmentation,Weighting,Computer science,Segmentation,Fuzzy logic,Pixel,Artificial intelligence,Gaussian function,Membership function,Coincident,Machine learning
Journal
Volume
Issue
ISSN
27
4
0954-0091
Citations 
PageRank 
References 
3
0.39
19
Authors
3
Name
Order
Citations
PageRank
Torki A. Altameem1324.64
ennumeri a zanaty2152.41
Amr Tolba317729.10