title: Robustness of local binary patterns in brain MR image analysis.
abstract: The aging population in developed countries has shifted considerable research attention to diseases related to age. Because age is one of the highest risk factors for neurodegenerative diseases, the need for automated brain image analysis has significantly increased. Magnetic Resonance Imaging (MRI) is a commonly used modality to image brain. MRI provides high tissue contrast; hence, the existing brain image analysis methods have often preferred the intensity information to others, such as texture. Recently, an easy-to-compute texture descriptor, Local Binary Pattern (LBP), has shown promise in various applications outside the medical field. In this paper, after extensive experiments, we show that rotation-invariant LBP is invariant to some common MRI artifacts that makes it possible to use it in various high-level brain MR image analysis applications.
authors.affiliation: Video Processing and Analysis Group, Philips Research Europe, 5656 AE, Eindhoven, The Netherlands. devrim.unay@philips.com
authors.forename: Devrim
authors.initials: D
authors.lastname: Unay
authors.forename: Ahmet
authors.initials: A
authors.lastname: Ekin
authors.forename: Mujdat
authors.initials: M
authors.lastname: Cetin
authors.forename: Radu
authors.initials: R
authors.lastname: Jasinschi
authors.forename: Aytul
authors.initials: A
authors.lastname: Ercil
mesh_terms: D000375:Aging; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000473:pathology*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods*; D015233:Models, Statistical; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted