TITLE:
An Adaptive Fuzzy C-Means Algorithm for Improving MRI Segmentation
AUTHORS:
Elnomery Allam Zanaty
KEYWORDS:
Fuzzy Clustering; Possiblistic C-Means; Medical Image Segmentation
JOURNAL NAME:
Open Journal of Medical Imaging,
Vol.3 No.4,
December
9,
2013
ABSTRACT: In this paper, we propose new fuzzy c-means method for improving the
magnetic resonance imaging (MRI) segmenta- tion. The proposed
method called “possiblistic fuzzy c-means (PFCM)” which hybrids the fuzzy c-means
(FCM) and possiblistic c-means (PCM) functions. It is realized by modifying the
objective function of the conventional PCM algorithm with Gaussian
exponent weights to produce memberships and possibilities simultaneously, along
with the usual point prototypes or cluster centers for each cluster. The
membership values can be interpreted as degrees of possibility of the points
belonging to the classes, i.e., the
compatibilities of the points with the class prototypes. For that, the proposed
algorithm is capable to avoid various problems of existing fuzzy clustering
methods that solve the defect of noise sensitivity and overcomes the coincident
clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated
by extensive segmentation experiments by applying them to the challenging
applications: gray matter/white matter segmentation in magnetic resonance image
(MRI) datasets and by comparison with other state of the art algorithms.
The experimental results show that the proposed method produces accurate and
stable results.