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, 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.

• 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