title: Spine labeling in axial magnetic resonance imaging via integral kernels.
abstract: This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images.
authors.affiliation: Simon Fraiser University, Burnaby, BC, Canada.
authors.forename: Brandon
authors.initials: B
authors.lastname: Miles
authors.affiliation: Ecole de Technologie Superieure (ETS), Montreal, QC, Canada. Electronic address: ismail.benayed@gmail.com.
authors.forename: Ismail
authors.initials: I
authors.lastname: Ben Ayed
authors.affiliation: University of Toronto, Toronto, ON, Canada.
authors.forename: Seyed-Parsa
authors.initials: SP
authors.lastname: Hojjat
authors.affiliation: McGill University, Montreal, QC, Canada.
authors.forename: Michael H
authors.initials: MH
authors.lastname: Wang
authors.affiliation: Western University, London, ON, Canada.
authors.forename: Shuo
authors.initials: S
authors.lastname: Li
authors.affiliation: Western University, London, ON, Canada.
authors.forename: Aaron
authors.initials: A
authors.lastname: Fenster
authors.affiliation: London Health Sciences Centre, London, ON, Canada.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Garvin
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008159:Lumbar Vertebrae / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results
keywords: Geometric priors; Integral kernels; Magnetic resonance imaging; Probability product kernels; Spine labeling