RESUMO
The segmentation of head images is useful to detect neuroanatomical structures and to follow and quantify the evolution of several brain lesions. 2D images correspond to brain slices. The more images are used the higher the resolution obtained is, but more processing power is required and parallelism becomes desirable. We present a new approach to segmentation of brain image volumes using DLML (Data List Management Library), a tool developed by our team. We organise the integer numbers identifying images into a list, and our DLML version process them both in parallel and with dynamic load balancing transparently to the programmer. We compare the performance of our DLML version to other typical parallel approaches developed with MPI (master-slave and static data distribution), using cluster configurations with 4-32 processors.