Alvarez Illan, Ignacio and Manuel Gorriz, Juan and Ramirez, Javier and Lang, Elmar W. and Salas-Gonzalez, Diego and Puntonet, Carlos G. (2012) Bilateral symmetry aspects in computer-aided Alzheimer's disease diagnosis by single-photon emission-computed tomography imaging. ARTIFICIAL INTELLIGENCE IN MEDICINE, 56 (3). pp. 191-198. ISSN 0933-3657,
Full text not available from this repository. (Request a copy)Abstract
Objective: This paper explores the importance of the latent symmetry of the brain in computer-aided systems for diagnosing Alzheimer's disease (AD). Symmetry and asymmetry are studied from two points of view: (i) the development of an effective classifier within the scope of machine learning techniques, and (ii) the assessment of its relevance to the AD diagnosis in the early stages of the disease. Methods: The proposed methodology is based on eigenimage decomposition of single-photon emission-computed tomography images, using an eigenspace extension to accommodate odd and even eigenvectors separately. This feature extraction technique allows for support-vector-machine classification and image analysis. Results: Identification of AD patterns is improved when the latent symmetry of the brain is considered, with an estimated 92.78% accuracy (92.86% sensitivity, 92.68% specificity) using a linear kernel and a leave-one-out cross validation strategy. Also, asymmetries may be used to define a test for AD that is very specific (90.24% specificity) but not especially sensitive. Conclusions: Two main conclusions are derived from the analysis of the eigenimage spectrum. Firstly, the recognition of AD patterns is improved when considering only the symmetric part of the spectrum. Secondly, asymmetries in the hypo-metabolic patterns, when present, are more pronounced in subjects with AD. (c) 2012 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | DIMENSIONAL PATTERN-CLASSIFICATION; SUPPORT VECTOR MACHINES; FDG-PET; COGNITIVE IMPAIRMENT; BRAIN PERFUSION; F-18-FDG PET; SPECT; DEMENTIA; IMAGES; PERFORMANCE; Principal-component analysis; Support-vector-machine; Computer-aided diagnosis; Alzheimer's disease |
| Subjects: | 500 Science > 530 Physics |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 04 May 2020 08:49 |
| Last Modified: | 04 May 2020 08:49 |
| URI: | https://pred.uni-regensburg.de/id/eprint/17825 |
Actions (login required)
 |
View Item |
