Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-03040-6_68 | Advances in Neuro-Information Processing |
Keywords | Field | DocType |
brain tissue,diagnostic tool,soft tissue,intensive need,classification algorithm,partial clustering,magnetic resonance imaging,tissue segmentation,semi-automatic identification,excellent spatial resolution,discriminative clustering,brain lesion,machine learning,spatial resolution,magnetic resonance image | Voxel,Discriminative clustering,Computer vision,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Soft tissue,Real-time MRI,Cluster analysis,Image resolution,Magnetic resonance imaging | Conference |
Volume | ISSN | Citations |
5507 | 0302-9743 | 1 |
PageRank | References | Authors |
0.39 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolau Gonçalves | 1 | 12 | 1.54 |
| Janne Nikkilä | 2 | 200 | 16.65 |
| Ricardo Vigário | 3 | 200 | 41.80 |