19 November 2013 An automatic search of Alzheimer patterns using a nonnegative matrix factorization
Author Affiliations +
Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 89220B (2013) https://doi.org/10.1117/12.2034415
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico
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).
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Diana L. Giraldo, Juan D. García-Arteaga, Eduardo Romero, "An automatic search of Alzheimer patterns using a nonnegative matrix factorization", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220B (19 November 2013); doi: 10.1117/12.2034415; https://doi.org/10.1117/12.2034415
PROCEEDINGS
8 PAGES


SHARE
Back to Top