Magnetic resonance imaging (MRI) is a imaging and diagnostic tool widely used, with excellent spatial resolution, and efficient in distinguishing between soft tissues. Here, we present a method for semi-automatic identification of brain tissues in MRI, based on a combination of machine learning approaches. Our approach uses self-organising maps (SOMs) for voxel labelling, which are used to seed the discriminative clustering (DC) classification algorithm. This method reduces the intensive need for a specialist, and allows for a rather systematic follow-up of the evolution of brain lesions, or their treatment. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Gonçalves, N., Nikkilä, J., & Vigário, R. (2009). Partial clustering for tissue segmentation in MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5507 LNCS, pp. 559–566). https://doi.org/10.1007/978-3-642-03040-6_68
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