Journal of Digital Imaging

, Volume 27, Issue 1, pp 98–107

Computer-Aided Diagnosis for Phase-Contrast X-ray Computed Tomography: Quantitative Characterization of Human Patellar Cartilage with High-Dimensional Geometric Features

Authors

    • Department of Biomedical EngineeringUniversity of Rochester
  • Paola Coan
    • Faculty of Medicine and Institute of Clinical RadiologyLudwig Maximilians University
    • Faculty of PhysicsLudwig Maximilians University
    • European Synchrotron Radiation Facility
  • Markus B. Huber
    • Department of Imaging SciencesUniversity of Rochester
  • Paul C. Diemoz
    • Faculty of PhysicsLudwig Maximilians University
    • European Synchrotron Radiation Facility
  • Christian Glaser
    • Faculty of Medicine and Institute of Clinical RadiologyLudwig Maximilians University
  • Axel Wismüller
    • Faculty of Medicine and Institute of Clinical RadiologyLudwig Maximilians University
    • Department of Imaging SciencesUniversity of Rochester
Article

DOI: 10.1007/s10278-013-9634-3

Cite this article as:
Nagarajan, M.B., Coan, P., Huber, M.B. et al. J Digit Imaging (2014) 27: 98. doi:10.1007/s10278-013-9634-3

Abstract

Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

Keywords

OsteoarthritisPhase-contrast imagingScaling index methodGray-level co-occurrence matrixSupport vector regression

Copyright information

© Society for Imaging Informatics in Medicine 2013