Morphometric data fusion for early detection of alzheimer’s disease

Abstract. We present a morphometry method which uses brain models generated using Nonnegative Matrix Factorization (NMF) characterized by signatures calculated from perceptual features such as intensities, edges and orientations, of some regions obtained by comparing the models. Two different measures are used to calculate volume-models distances in the regions of interest. The discerning power of these distances is tested by using them as features for a Support Vector Machine classifier. This work shows the usefulness of both measures as metrics in medical image applications when they are used in binary classification tasks. Our methodology was tested with two experimental groups extracted from a public brain MR dataset (OASIS), the classification between healthy subjects and patients with mild AD reveals an equal error rate (EER) measure which is better than previous approaches tested on the same dataset (0.1 in the former and 0.2 in the latter). When detecting very mild AD, our results (near to 75% of sensitivity and specificity) are comparable to the results with those approaches.

• Autores: Giraldo Franco, Diana Lorena
• Fecha de publicación: 2015
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/54346
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/54346; http://bdigital.unal.edu.co/49248/
• Palabra clave: 6 Tecnología (ciencias aplicadas) / Technology; 61 Ciencias médicas; Medicina / Medicine and health; 62 Ingeniería y operaciones afines / Engineering; Alzheimer’s Disease; MRI; Morphometry; NMF; Pattern Recognition; KullbackLeibler Divergence; Earth Mover’s Distance; Enfermedad de Alzheimer; IRM,; Morfometría; NMF; Reconocimiento de Patrones; Divergencia de Kullback-Leibler; EMD
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional