The cross section area of spinal canal has been an important indicator for lumbar spinal stenosis (LSS), which remains the leading preoperative diagnosis for adults older than 65 years. Due to its irregularity in spatial shape and lack of spectral information, this region can only be defined by doctors manually and calculated the amount of area by commercial software at present. The solution for reliable and robust classification and measurement remains open. This manuscript utilized kernel-based support vector machine to provide an automatically classification and measurement of the cross-section area of spinal canal. This kernel-based SVM classifier is compared with the linear SVM proposed in [1] and the present method. The experiments showed that the kernel based-SVM classifier could provide a better performance and robust classification result for the cross section area of spinal canal.

Original languageEnglish
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Number of pages4
Publication statusPublished - 2012
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: Oct 14 2012Oct 17 2012


Other2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Country/TerritoryKorea, Republic of


  • Classification
  • Kernel function
  • Radial basis function (RBF)
  • Spinal Canal
  • Support Vector Machine (SVM)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction


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