title: Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features.
abstract: OBJECTIVE
  Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections.
  
  
  METHODS
  A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification.
  
  
  RESULTS
  The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image.
  
  
  CONCLUSIONS
  A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for detecting the laminae and facet joints in ultrasound images has been proposed. The system has the potential to assist the anesthesiologists in quickly finding the target plane for epidural steroid injections and facet joint injections.
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada, mehranp@ece.ubc.ca.
authors.forename: Mehran
authors.initials: M
authors.lastname: Pesteie
authors.forename: Purang
authors.initials: P
authors.lastname: Abolmaesumi
authors.forename: Hussam Al-Deen
authors.initials: HA
authors.lastname: Ashab
authors.forename: Victoria A
authors.initials: VA
authors.lastname: Lessoway
authors.forename: Simon
authors.initials: S
authors.lastname: Massey
authors.forename: Vit
authors.initials: V
authors.lastname: Gunka
authors.forename: Robert N
authors.initials: RN
authors.lastname: Rohling
mesh_terms: D000775:Anesthesia, Spinal / Q000379:methods*; D001416:Back Pain / Q000000981:diagnostic imaging / Q000188:drug therapy*; D006801:Humans; D007268:Injections, Epidural; D018084:Ultrasonography, Interventional / Q000379:methods*; D021801:Zygapophyseal Joint / Q000000981:diagnostic imaging*