Abstract
A novel application of rough-fuzzy clustering is presented for synthetic as well as CT scan images of the brain. It is observed that the algorithm generates good prototypes even in the presence of outliers. The rough-fuzzy clustering simultaneously handles overlap of clusters and uncertainty involved in class boundary, thereby yielding the best approximation of a given structure in unlabeled data. The number of clusters is automatically optimized in terms of various validity indices. A comparative study is made with related partitive algorithms. Experimental results demonstrate the diagnosis of the extent of brain infarction in CT scan images, and is validated by medical experts.
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Mitra, S., Barman, B. (2008). Rough-Fuzzy Clustering: An Application to Medical Imagery. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_43
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DOI: https://doi.org/10.1007/978-3-540-79721-0_43
Publisher Name: Springer, Berlin, Heidelberg
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