title: Exploratory matrix factorization for PET image analysis.
abstract: Features are extracted from PET images employing exploratory matrix factorization techniques such as nonnegative matrix factorization (NMF). Appropriate features are fed into classifiers such as a support vector machine or a random forest tree classifier. An automatic feature extraction and classification is achieved with high classification rate which is robust and reliable and can help in an early diagnosis of Alzheimer's disease.
authors.affiliation: Univ. of Regensburg, Germany.
authors.forename: A
authors.initials: A
authors.lastname: Kodewitz
authors.forename: I R
authors.initials: IR
authors.lastname: Keck
authors.forename: A M
authors.initials: AM
authors.lastname: Tomé
authors.forename: E W
authors.initials: EW
authors.lastname: Lang
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging; D003072:Cognition Disorders / Q000000981:diagnostic imaging; D016208:Databases, Factual; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*