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.

Image recognition and classification have been widely used for research in computer vision systems. This paper aims to implement a new strategy called Wiener-Granger Causality theory for classifying natural scenery images. This strategy is based on self-content images extracted using a Content-Based Image Retrieval (CBIR) methodology (to obtain different texture features); later, a Genetic Algorithm (GA) is implemented to select the most relevant natural elements from the images which share similar causality patterns. The proposed method is comprised of a sequential feature extraction stage, a time series conformation task, a causality estimation phase, causality feature selection throughout the GA implementation (using the classification process into the fitness function). A classification stage was implemented and 700 images of natural scenery were used for validating the results. Tested in the distribution system implementation, the technical efficiency of the developed system is 100% and 96% for resubstitution and cross-validation methodologies, respectively. This proposal could help with recognizing natural scenarios in the navigation of an autonomous car or possibly a drone, being an important element in the safety of autonomous vehicles navigation.