Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation

Ajala Funmilola A, Oke O.A, Adedeji T.O, Alade O.M, Adewusi E.A


Medical image segmentation is an initiative with tremendous usefulness. Biomedical and anatomical information are made easy to obtain as a result of success achieved in automating image segmentation. More research and work on it has enhanced more effectiveness as far as the subject is concerned. Several methods are employed for medical image segmentation such as Clustering methods, Thresholding method, Classifier, Region Growing, Deformable Model, Markov Random Model etc. This work has mainly focused attention on Clustering methods, specifically k-means and fuzzy c-means clustering algorithms. These algorithms were combined together to come up with another method called fuzzy k-c-means clustering algorithm, which has a better result in terms of time utilization. The algorithms have been implemented and tested with Magnetic Resonance Image (MRI) images of Human brain. Results have been analyzed and recorded. Some other methods were reviewed and advantages and disadvantages have been stated as unique to each. Terms which have to do with image segmentation have been defined along side with other clustering methods.

Keywords: Clustering algorithms, Fuzzy c-means, K-means, Segmentation

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ISSN (Paper)2224-5782 ISSN (Online)2225-0506
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