Abstract
In this paper we introduce a new fuzzy c-means objective function called kernel induced fuzzy c-means based on Gaussian function for the purpose of segmentation of medical images. The originality of this algorithm is based on the fact that the conventional FCM-based algorithm considers no spatial c ontent information, which makes it sensitive to noise. The new algorithm is formulated by incorporating the spatial neighbourhood information into the spatial neighbourhood into the original FCM algorithm by apriori probability and initialized by a kernel function induced histogram based FCM algorithm. The probability in the algorithm that indicates the spatial influence of the neighbouring pixels on the centre pixel plays a key role in this algorithm and it obtains efficient method for calculating membership and updating prototypes by minimizing the new objective function of Gaussian based fuzzy c-means. The performance of proposed algorithm has been tested with medical images to reduce the inhomogeneities and to allow the labelling of a pixel to be influenced by the labels in its immediate neighbourhood. Also this paper compares the results of proposed method with the results of existing basic fuzzy c-means.
Published Version
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