Image segmentation using fuzzy clustering means techniques

Simulation results have confirmed the possibility to reduce significantly the time spent in the image segmentation for small size images without decreasing of segmentation accuracy. The behavior of the method in big images regarding the value of the difference D is under the expected. In future, it is expected to correct the proposed approach applying novel fuzzy logics rules. The tool used in this work for the segmentation process in medical imaging is the fuzzy logic theory, or more precisely, the Fuzzy Clustering Means (FCM) algorithm, which is one of the most effective methods applied in the image segmentation. This algorithm is feed with the number of clusters that we want to create and searches for its centers to determine quantity of data belong to each a cluster.