title: Tensor-product kernel-based representation encoding joint MRI view similarity.
abstract: To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.
authors.forename: A
authors.initials: A
authors.lastname: Alvarez-Meza
authors.forename: D
authors.initials: D
authors.lastname: Cardenas-Pena
authors.forename: A E
authors.initials: AE
authors.lastname: Castro-Ospina
authors.forename: M
authors.initials: M
authors.lastname: Alvarez
authors.forename: G
authors.initials: G
authors.lastname: Castellanos-Dominguez
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D011859:Radiography; D012737:Sex Factors