Sparse Representation for Different Animal Vertebra Classification along the Fixation Trajectory of Pedicle Screw

Abstract

Pedicle screw (PS) implantation is an ideal method for the treatment of severe multilevel vertebral instability. The key problem is the accuracy of PS fixation. In this paper, the spectrum of different tissues along the fixation trajectory of PS is studied to tackle the accuracy problem. Fresh porcine vertebrae, bovine vertebrae, and ovine vertebrae were measured by using the near-infrared spectrum (NIRs) device to obtain the reflected spectrum from these vertebrae. Along the fixation trajectory of PS, the classification method based on the sparse representation-based classifier (SRC) was applied to different vertebral tissues (cortical bones and cancellous bones). Considering the large amount of spectral data, sparse preserving projection (SPP) was applied to improve the performance of SRC. The proposed method based on the SPP method for dimensionality reduction and the SRC method for tissue recognition was first used in vertebrae classification and showed superior performance compared with other classification methods, such as SVM and 1NN. The results gained from this project are vital significant to the development of hi-tech medical instruments with independent intellectual property rights.