Loading...

Towards an automatic diagnosis system for lumbar disc herniation: the significance of local subset feature selection

Ebrahimzadeh, E ; Sharif University of Technology | 2018

1230 Viewed
  1. Type of Document: Article
  2. DOI: 10.4015/S1016237218500448
  3. Publisher: World Scientific Publishing Co. Pte Ltd , 2018
  4. Abstract:
  5. Herniation in the lumbar area is one of the most common diseases which results in lower back pain (LBP) causing discomfort and inconvenience in the patients' daily lives. A computer aided diagnosis (CAD) system can be of immense benefit as it generates diagnostic results within a short time while increasing precision of diagnosis and eliminating human errors. We have proposed a new method for automatic diagnosis of lumbar disc herniation based on clinical MRI data. We use T2-W sagittal and myelograph images. The presented method has been applied on 30 clinical cases, each containing 7 discs (210 lumbar discs) for the herniation diagnosis. We employ Otsu thresholding method to extract the spinal cord from MR images of lumbar disc. A third order polynomial is then aligned on the extracted spinal cords, and by the end of preprocessing stage, all the T2-W sagittal images will have been prepared for specifying disc boundary and labeling. Having extracted an ROI for each disc, we proceed to use intensity and shape features for classification. The extracted features have been selected by Local Subset Feature Selection. The results demonstrated 91.90%, 92.38% and 95.23% accuracy for artificial neural network, K-nearest neighbor and support vector machine (SVM) classifiers respectively, indicating the superiority of the proposed method to those mentioned in similar studies. © 2018 National Taiwan University
  6. Keywords:
  7. Automatic diagnosis ; KNN ; Local subset feature selection ; Lumbar disc diseases ; Spinal cord ; SVM ; Classification (of information) ; Feature extraction ; Image processing ; Laser tissue interaction ; Magnetic resonance imaging ; Nearest neighbor search ; Neural networks ; Program diagnostics ; Support vector machines ; Computer Aided Diagnosis(CAD) ; K-nearest neighbors ; Lumbar disc ; Lumbar disc herniation ; Otsu thresholding ; Pre-processing stages ; Spinal cords ; Computer aided diagnosis
  8. Source: Biomedical Engineering - Applications, Basis and Communications ; 2018 ; 10162372 (ISSN)
  9. URL: https://www.worldscientific.com/doi/abs/10.4015/S1016237218500448