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Proceedings Paper

An automatic search of Alzheimer patterns using a nonnegative matrix factorization
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Paper Abstract

This paper presents a fully automatic method that condenses relevant morphometric information from a database of magnetic resonance images (MR) labeled as either normal (NC) or Alzheimer's disease (AD). The proposed method generates class templates using Nonnegative Matrix Factorization (NMF) which will be used to develop an NC/AD classi cator. It then nds regions of interest (ROI) with discerning inter-class properties. by inspecting the di erence volume of the two class templates. From these templates local probability distribution functions associated to low level features such as intensities, orientation and edges within the found ROI are calculated. A sample brain volume can then be characterized by a similarity measure in the ROI to both the normal and the pathological templates. These characteristics feed a simple binary SVM classi er which, when tested with an experimental group extracted from a public brain MR dataset (OASIS), reveals an equal error rate measure which is better than the state-of-the-art tested on the same dataset (0:9 in the former and 0:8 in the latter).

Paper Details

Date Published: 19 November 2013
PDF: 8 pages
Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220B (19 November 2013); doi: 10.1117/12.2034415
Show Author Affiliations
Diana L. Giraldo, Univ. Nacional de Colombia (Colombia)
Juan D. García-Arteaga, Univ. Nacional de Colombia (Colombia)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)

Published in SPIE Proceedings Vol. 8922:
IX International Seminar on Medical Information Processing and Analysis
Jorge Brieva; Boris Escalante-Ramírez, Editor(s)

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