Incorporating prior information in the fuzzy C-mean algorithm with application to brain tissues segmentation in MRI

Authors:
Moumen El-Melegy;Hashim Mokhtar
Affiliations:
Electrical Engineering Department, Assiut University, Assiut, Egypt;Electrical Engineering Department, Assiut University, Assiut, Egypt
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 2
Cited 0

Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

Pattern Recognition
Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

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Abstract

This paper introduces a new formula for the objective function of the famous fuzzy C-means algorithm. Two weighted terms are added to the objective function to reflect any available information about the class center and class pixels distribution throughout the datasets. The algorithm is evaluated for the task of the segmentation of medical MRI brain volume. The results show that the algorithm has a considerable robustness against noise and partial volume effects, and it needs a smaller number of iterations to reach convergence compared with other similar algorithms.