title: Bilateral symmetry aspects in computer-aided Alzheimer's disease diagnosis by single-photon emission-computed tomography imaging.
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.
authors.affiliation: Department of Signal Theory, Networking and Communications, Escuela Técnica Superior de Ingeniería Informática y Telecomunicaciones, University of Granada, Spain. illan@ugr.es
authors.forename: Ignacio Alvarez
authors.initials: IA
authors.lastname: Illán
authors.forename: Juan Manuel
authors.initials: JM
authors.lastname: Górriz
authors.forename: Javier
authors.initials: J
authors.lastname: Ramírez
authors.forename: Elmar W
authors.initials: EW
authors.lastname: Lang
authors.forename: Diego
authors.initials: D
authors.lastname: Salas-Gonzalez
authors.forename: Carlos G
authors.initials: CG
authors.lastname: Puntonet
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D001921:Brain / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D012737:Sex Factors; D060388:Support Vector Machine; D015899:Tomography, Emission-Computed, Single-Photon