title: Automatic spinal canal detection in lumbar MR images in the sagittal view using dynamic programming.
abstract: As there is an increasing need for the computer-aided effective management of pathology in lumbar spine, we have developed a computer-aided diagnosis and characterization framework using lumbar spine MRI that provides radiologists a second opinion. In this paper, we propose a left spinal canal boundary extraction method, based on dynamic programming in lumbar spine MRI. Our method fuses the absolute intensity difference of T1-weighted and T2-weighted sagittal images and the inverted gradient of the difference image into a dynamic programming scheme and works in a fully automatic fashion. The boundaries generated by our method are compared against reference boundaries in terms of the Euclidean distance and the Chebyshev distance. The experimental results from 85 clinical data show that our methods find the boundary with a mean Euclidean distance of 3mm, achieving a speedup factor of 167 compared with manual landmark extraction. The proposed method successfully extracts landmarks automatically and fits well with our framework for computer-aided diagnosis in lumbar spine.
authors.affiliation: Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14228, USA. Electronic address: jkoh@buffalo.edu.
authors.forename: Jaehan
authors.initials: J
authors.lastname: Koh
authors.affiliation: Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14228, USA. Electronic address: vipin@buffalo.edu.
authors.forename: Vipin
authors.initials: V
authors.lastname: Chaudhary
authors.affiliation: Department of Chemistry, State University of New York at Buffalo, Buffalo, NY 14260, USA. Electronic address: eunjeon@buffalo.edu.
authors.forename: Eun Kyung
authors.initials: EK
authors.lastname: Jeon
authors.affiliation: ProScan Imaging Buffalo, Williamsville, NY 14221, USA. Electronic address: gdhillon@proscan.com.
authors.forename: Gurmeet
authors.initials: G
authors.lastname: Dhillon
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008159:Lumbar Vertebrae / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D056888:Patient Positioning / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011187:Posture; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013115:Spinal Canal / Q000033:anatomy & histology*
keywords: Boundary extraction; Computer-aided diagnosis; Lumbar spine; Magnetic resonance imaging