Liver CT Image Segmentation with an Optimum Threshold using Measure of Fuzziness

Citation:
Abder-Rahman Ali, Micael Couceiro, Ahmed M. Anter, A. E. Hassenian, M. F. Tolba, and V. Snasel, "Liver CT Image Segmentation with an Optimum Threshold using Measure of Fuzziness", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications, 22-24 June 2014, , Ostrava, Czech Republic., 22-24 June , 2014.

Date Presented:

22-24 June

This paper presents a Fuzzy C-Means based image segmentation
approach with an optimum threshold using measure of fuzziness.
The optimized version, herein denoted as FCM-t, bene ts from an optimum
threshold, calculated using measure of fuzziness. This allows the
revealing of ambiguous pixels, which are eventually assigned to the appropriate
clusters by calculating the rounded average cluster values in
the ambiguous pixels neighbourhood. The proposed approach showed
signi cantly better results compared to the traditional Fuzzy C-Means,
at the cost of some processing power. By bene ting from the optimum
threshold approach, one is able to increase the segmentation performance
by approximately three times more than with the traditional FCM.

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