Image segmentation is an established necessity for an improved analysis of Magnetic Resonance images. Neural network-based clustering has been shown in literature to yield good results, yet the possibility of transforming the input feature space in order to enhance the clustering process has gone largely unexplored. In this paper we focus on brain imaging and present a new algorithm for unsupervised segmentation of multi-spectral images, based on the research, through neuro-fuzzy techniques, of an optimized space in which to perform clustering. Tests performed on both real and simulated MR images show promising results, encouraging the application to different medical targets and further investigation.
['Lia Morra', 'Silvia Delsanto', 'Leonardo Reyneri']
Enhanced unsupervised segmentation of multispectral Magnetic Resonance images