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

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 f...

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Bibliographic Details
Published in2009 16th IEEE International Conference on Image Processing (ICIP) pp. 3393 - 3396
Main Authors El-Melegy, M., Mokhtar, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Acceleration
Biomedical imaging
Clustering algorithms
Convergence
Fuzzy classification
Fuzzy systems
Image segmentation
Labeling
Magnetic resonance imaging
MRI segmentation
Noise robustness
Pixel
prior information
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Summary: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.
ISBN:9781424456536
1424456533
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2009.5413871