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Fuzzy C-Means (FCM) algorithm is one of the commonly preferred fuzzy algorithms for image segmentation applications. Even though FCM algorithm is sufficiently accurate, it suffers from the computational complexity problem which prevents the usage of FCM in real-time applications. In this work, this convergence problem is tackled through the proposed Modified FCM (MFCM) algorithm. In this algorithm, several clusters among the input data are formed based on similarity measures and one representative data from each cluster is used for FCM algorithm. Hence, this methodology minimizes the convergence time period requirement of the conventional FCM algorithm to higher extent. This proposed approach is experimented on Magnetic Resonance (MR) brain tumor images. Experimental results suggest promising results for the MFCM algorithm in terms of the performance measures.

This paper describes a method how to represent and build a reusable VHDL component. By that component we can, for example, describe a family of the relative VHDL models. To represent the component, we use external functions as a mechanism to support a pre-processing and perform the instantiation of the component. A user interface, the constituent of the reusable component, serves for transferring parameters for the instantiation. We deliver a formal syntax of the functions and examples of their semantics. We describe the design of the reusable component as a procedure of transferring of: a) the intrinsic characteristics for a given family of domain objects and b) features from a given VHDL model(s). Those features require to be re-coded and extended with new ones by means of the external functions introduced. To test a reusable component, we use pre-processing and modelling.