Saliency-based characterization of group differences for magnetic resonance disease classification
Anatomical variability of patient's brains limits the statistical analyses about presence or absence of a pathology. In this paper, we present an approach for classification of brain Magnetic Resonance (MR) images from healthy and diseased subjects. The approach builds up a saliency map, which...
- Autores:
-
Rueda Olarte, Andrea del Pilar
González Osorio, Fabio Augusto
Romero Castro, Eduardo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2013
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/39493
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/39493
http://bdigital.unal.edu.co/29590/
- Palabra clave:
- Subject classification
Magnetic Resonance Imaging
Visual Attention models
Saliency maps
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Anatomical variability of patient's brains limits the statistical analyses about presence or absence of a pathology. In this paper, we present an approach for classification of brain Magnetic Resonance (MR) images from healthy and diseased subjects. The approach builds up a saliency map, which extract regions of relative change in three different dimensions: intensity, orientation and edges. The obtained regions of interest are used as suitable patterns for subject classification using support vector machines. The strategy’s performance was assessed on a set of 198 MR images extracted from the OASIS database and divided into four groups, reporting an average accuracy rate of 74.54% and an average Equal Error Rate of 0.725. |
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