Speeding up small sized self-organizing maps for use in visualization of multispectral medical images

We present the results of parallel implementations of Kohonen's self-organizing maps using data partitioning. Two algorithms are implemented, a pure data partitioning algorithm and a combined data- and network-partitioning algorithm. The performance of the algorithms is far better for small neural networks than the performance of our previous SOM implementations. The SOM model can be used for visualization of MR images, an application with a small number of neurons. Using one of the proposed algorithms, the performance of this application is increased by over 200%. The convergence rate of the proposed algorithm and the original algorithm is shown to be similar when the frequency of the weight update is properly selected.<>