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A fully automatic tool to assist the segmentation of brain magnetic resonance images (MRI) is presented. Thus, the figured out regions can be evaluated for the diagnosis of brain disorders. The main problem to be handled consists in discovering different regions on the image without using apriori information. The new approach consists in hybridising multiobjective optimisation for feature selection with a growing hierarchical self-organising map (GHSOM) classifier and a probability clustering method. The segmentation results yield average overlap metric values of 0.32, 0.75 and 0.69 for white matter, grey matter and cerebrospinal fluid, respectively, over the Internet Brain Segmentation Repository database. These results mean an improvement over the values reached by other existing techniques.
Inspec keywords: image segmentation; medical image processing; magnetic resonance imaging; probability
Other keywords:
Subjects: Magnetic resonance spectrometers, auxiliary instruments and techniques; Probability theory, stochastic processes, and statistics; Image processing and restoration; Medical magnetic resonance imaging and spectroscopy; Other topics in statistics; Optical, image and video signal processing