Automatic classification of structural mri for diagnosis of neurodegenerative diseases
This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, auto...
- Autores:
-
Diaz, Gloria
Romero, Eduardo
Hernández-Tamames, Juan Antonio
Molina, Vicente
Malpica, Norberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2010
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/30471
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/30471
http://bdigital.unal.edu.co/20547/
http://bdigital.unal.edu.co/20547/2/
- Palabra clave:
- Neurodegenerative disease
structural MRI
pattern classification
SPM
VBM
DARTEL
support vector machines.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples. |
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