Modified fuzzy c-means algorithm for segmentation of T1-T2-weighted brain MRI

Authors:
S. Ramathilagam;R. Pandiyarajan;A. Sathya;R. Devi;S. R. Kannan
Affiliations:
Department of Engineering Science, National Cheng Kung University, Tainan 70701, Taiwan;Department of Mathematics, GRU, India;Department of Mathematics, GRU, India;Department of Mathematics, GRU, India;Department of Electrical Engineering, National Cheng Kung University, Tainan 70701, Taiwan and Department of Mathematics, GRU, India
Venue:
Journal of Computational and Applied Mathematics
Year:
2011

Citing 12
Cited 1

Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence theory for fuzzy c-means: counterexamples and repairs

IEEE Transactions on Systems, Man and Cybernetics
Optimal discriminant plane for a small number of samples and design method of classifier on the plane

Pattern Recognition
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A New Variational Shape-from-Orientation Approach to Correcting Intensity Inhomogeneities in MR Images

WBIA '98 Proceedings of the IEEE Workshop on Biomedical Image Analysis
An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI

Pattern Recognition Letters
A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

Pattern Recognition
A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction

Pattern Recognition Letters
A new segmentation system for brain MR images based on fuzzy techniques

Applied Soft Computing
Novel modified fuzzy c-means algorithm with applications

Digital Signal Processing
Hybrid algorithms with instance-based classification

ECML'05 Proceedings of the 16th European conference on Machine Learning
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence

Segmentation of brain MR images using intuitionistic fuzzy clustering algorithm

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

Quantified Score

Hi-index	7.29

Visualization

Abstract

In the past decades, fuzzy segmentation methods, especially the fuzzy c-means algorithms, have been widely used in the segmentation of brain medical images, because they can preserve more information from the original image than other segmentation methods. This paper introduces a modified robust fuzzy c-means algorithm with special weighted bias estimation for segmentation of brain magnetic resonance image. In general, the intensity inhomogeneities are endorsed to imperfections in the radio-frequency coils or to the problems connected with the image acquisition. The intensity inhomogeneities are seriously affecting the segmentation process when computer assisted segmentation algorithms used in differentiating borders between the tissues in medical images. The proposed algorithm of this paper is considered the fact in the standard fuzzy c-means with no spatial context information, which makes it sensitive to noise. Hence the proposed method is capable to deal with the intensity inhomogeneities and noised image effectively. Further, to reduce the number of iterations, the proposed modified robust algorithm initializes the centroid using dist-max initialization algorithm before the execution of algorithm iteratively. Experimental results carried out through the comparative studies of segmentation results of the proposed method and the segmentation results of other existed algorithms when these algorithms implemented on brain magnetic resonance image and benchmark lung cancer dataset. The superiority of the proposed method has shown from the experimental results.