@inproceedings{b2de7aa45a4640ed8b7e91ce83511380,
title = "Classification of cross-section area of spinal canal on kernel-based support vector machine",
abstract = "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.",
keywords = "Classification, Kernel function, Radial basis function (RBF), Spinal Canal, Support Vector Machine (SVM)",
author = "Wu, {Chao Cheng} and Li, {Hsiao Chi} and Chiang, {Yung Hsiao} and Jiannher Lin",
year = "2012",
doi = "10.1109/ICSMC.2012.6378142",
language = "English",
isbn = "9781467317146",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
pages = "2622--2625",
booktitle = "Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012",
note = "2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 ; Conference date: 14-10-2012 Through 17-10-2012",
}