Partial Clustering for Tissue Segmentation in MRI

Authors:
Nicolau Gonçalves;Janne Nikkilä;Ricardo Vigário
Affiliations:
Adaptive Informatics Research Centre, Helsinki University of Technology, Finland FIN-02015;Adaptive Informatics Research Centre, Helsinki University of Technology, Finland FIN-02015;Adaptive Informatics Research Centre, Helsinki University of Technology, Finland FIN-02015
Venue:
Advances in Neuro-Information Processing
Year:
2009

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Clustering through SOM consistency

ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I

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Abstract

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.