Abstract
Recent advances in multi-parameter MR brain imaging has enabled multi-class tissue characterization for better quantitative analysis and understanding brain disorders and pathologies. This paper presents a maximum likelihood based method for multi-class segmentation that utilizes spatio-frequency features obtained from wavelet analysis along with the multi-parameter measurements. Results on MR brain images of a patient with stroke are presented.
Original language | English (US) |
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Title of host publication | 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 |
Pages | 222-225 |
Number of pages | 4 |
DOIs | |
State | Published - Oct 27 2009 |
Event | 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 - Antalya, Turkey Duration: Apr 29 2009 → May 2 2009 |
Other
Other | 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 |
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Country/Territory | Turkey |
City | Antalya |
Period | 4/29/09 → 5/2/09 |
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Clinical Neurology
- Neuroscience(all)