title: A robust volumetric feature extraction approach for 3D neuroimaging retrieval.
abstract: The increased volume of 3D neuroimaging data has created a need for efficient data management and retrieval. We suggest that image retrieval via robust volumetric features could benefit managing these large image datasets. In this paper, we introduce a new feature extraction method, based on disorder-oriented masks, that uses the volumetric spatial distribution patterns in 3D physiological parametric neurological images. Our preliminary results indicate that the proposed volumetric feature extraction approach could support reliable 3D neuroimaging data retrieval and management.
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia.
authors.forename: Sidong
authors.initials: S
authors.lastname: Liu
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.forename: Lingfeng
authors.initials: L
authors.lastname: Wen
authors.forename: Stefan
authors.initials: S
authors.lastname: Eberl
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fulham
authors.forename: Dagan
authors.initials: D
authors.lastname: Feng
mesh_terms: D000368:Aged; D000465:Algorithms; D016208:Databases, Factual; D003704:Dementia / Q000175:diagnosis*; D003952:Diagnostic Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D008875:Middle Aged; D049268:Positron-Emission Tomography