We are interested in running in parallel cellular automata. We present an algorithm which explores the dynamic remapping of cells in order to balance the load between processing nodes. The parallel application runs on a cluster of PCs connected by Fast-Ethernet. A general cellular automaton can be described as a set of cells where each cell is a state machine. To compute the next cell state, each cell needs some information from neighbouring cells. There are no limitations on the kind of information exchanged nor on the computation itself. Only the automaton topology defining the neighbours of each cell remains unchanged during the automaton's life. As a typical example of cellular automation we consider the image skeletonization problem. Skeletonization requires spatial filtering to be repetitively applied to the image. Each step erodes a thin part of the original image. After the last step, only the image skeleton remains. Skeletonization algorithms require vast amounts of computing power, especially when applied to large images. Therefore, skeletonization application can potentially benefit from the use of parallel processing. Two different parallel algorithms are proposed, one with a static load distribution consisting in splitting the cells over several processing nodes and the other with a dynamic load balancing scheme capable of remapping cells during the program execution. Performance measurements show that the cell migration doesn't reduce the speedup if the program is already load balanced. It greatly improves the performance if the parallel application is not well balanced