Utilization of Spatial Coherence in Functional Neuroimage-Based Classification
| Autor: | Rebecca McNamee, Pinaki Mitra, Vanathi Gopalakrishnan |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
medicine.diagnostic_test
Contextual image classification business.industry Computer science Brain activity and meditation Feature extraction Feature selection computer.software_genre Machine learning Support vector machine Feature (computer vision) Voxel medicine Artificial intelligence Functional magnetic resonance imaging business computer |
| Zdroj: | 2009 3rd International Conference on Bioinformatics and Biomedical Engineering. |
| DOI: | 10.1109/icbbe.2009.5163742 |
| Popis: | Functional magnetic resonance imaging provides a non-invasive mechanism for monitoring brain activity of subjects during performance of a task. While this approach has been used extensively for human brain mapping activities, automated classification of subjects based on neural activation patterns is also of interest. However, due to the high dimensionality of the image data, classification accuracy is highly dependent upon the adequacy of the features used in the models. In this work 1 , we present a new feature refinement strategy that uses spatial coherence information to eliminate irrelevant features from consideration. For a neurobehavioral disinhibition dataset, we show that this new approach for feature selection using spatially coherent voxels (SCV) outperforms conventional methods. |
| Databáze: | OpenAIRE |
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