Abstract
This paper presents a rough kernelized fuzzy c-means clustering (RKFCM) based medical image segmentation algorithm. It is a combination of rough set and kernelized FCM clustering (KFCM). KFCM introduced new technique of clustering using kernel induced distance and improved its robustness towards noise. However, it is failed to remove the vagueness and uncertainty of the clustering technique. In this paper, we use rough set with KFCM for removal of uncertainty by introduction of higher and lower estimation of rough set theory. The objective function derived from KFCM is merged with rough set to get better segmentation results. Experiments performed on numerous medical image data sets and its resulting validity index values have proved this algorithm to be more efficient in comparison to existing algorithms.
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Halder, A., Guha, S. (2017). Rough Kernelized Fuzzy C-Means Based Medical Image Segmentation. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_36
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DOI: https://doi.org/10.1007/978-981-10-6430-2_36
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