Parallel c-means algorithm for image segmentation on a reconfigurable mesh computer

In this paper, we propose a parallel algorithm for data classification, and its application for Magnetic Resonance Images (MRI) segmentation. The studied classification method is the well-known c-means method. The use of the parallel architecture in the classification domain is introduced in order to improve the complexities of the corresponding algorithms, so that they will be considered as a pre-processing procedure. The proposed algorithm is assigned to be implemented on a parallel machine, which is the reconfigurable mesh computer (RMC). The image of size (m × n) to be processed must be stored on the RMC of the same size, one pixel per processing element (PE).

► Parallel algorithm for standard c-means clustering problem on reconfigurable mesh computer.
► Parallel algorithm for Magnetic Resonance Images (MRI) segmentation.
► The massively parallel SIMD architectures are well suited for parallel image processing.
► Dynamic evolution of clusters cardinalities during the execution of the clustering program.
► The reconfiguration operations allow effective data exchange over the RMESH matrix.