Optimized segmentation of Brain MRI using GHSOM and evolutive computing
Ortiz Andres, J. M. Górriz, Javier Ramı́rez, Diego Salas-Gonzalez · Frontiers in artificial intelligence and applications · 2012
Image segmentation aims to partition an image into its constituent parts. In Magnetic Resonance Imaging (MRI), image segmentation groups the voxels belonging to a specific tissue or fluids. Currently, this is an essential step in MRI analysis and provides a way to diagnose many neurological disorders such as the Alzheimer disease. In this paper we present a segmentation method for MRI based on the Growing Hierarchical Self-Organizing Map and multiobjective-based feature selection to optimize the performance of the segmentation process. Since the features extracted from the image result crucial for the final performance of the segmentation process, the optimized features which maximize the performance of the segmentation process on each plane are used. The experiments performed have been carried out using the Internet Brain Segmentation Repository (IBSR) as it has been widely used in previous works. Thus, the comparisons with other methods carried out using the IBSR database, show that our method outperforms other algorithms.